Location: Hyderabad, India
Location:
India (100% Remote / Flexible)
Contract Type:
Full-Time / Enterprise Project Engagement
About Xenon7
Where elite tech talent meets world-class opportunities! At Xenon7, we work with leading enterprise clients and innovative startups on high-impact projects across Data, AI, Cloud, and Software Engineering. Our expertise in AI solution architecture and specialized technical talent allows us to partner with enterprise leaders on transformative initiatives, driving innovation and business growth.
Job Summary
We are seeking a high-caliber
Forward Deployment Engineer (FDE)
to sit at the intersection of enterprise AI platforms and executive Finance business functions for our Fortune 100 enterprise client. This role bridges advanced AI platform engineering with direct business impact—translating ambiguous financial challenges into working AI applications, deploying them into regulated client environments, and iterating in tight loops to drive measurable ROI.
Rather than building static models or waiting for formal spec sheets, you will operate as a product-minded technical lead. You will prototype user-facing tools in days, deploy agentic and RAG workflows on top of enterprise data stacks (Databricks Genie), and ensure high adoption across FP&A, Controllership, Treasury, and CFO leadership.
Key Responsibilities
End-to-End AI Application Prototyping & Delivery
Own the full lifecycle of AI solutions for Finance business units—from initial discovery with CFO stakeholders to production deployment, user adoption, and ROI measurement.
Rapidly prototype vertical applications using Streamlit, Databricks Apps, FastAPI/Flask, or lightweight React interfaces within 1–2 weeks to gather real user feedback.
Handle "last mile" execution: edge cases, data quirks, business rule exceptions, and user training to turn prototypes into sticky enterprise products.
Agentic Systems & LLM Engineering
Build production-grade vertical AI tools leveraging Databricks Genie (Genie Spaces, semantic models).
Construct robust RAG pipelines incorporating advanced chunking, vector databases (Pinecone, Chroma, FAISS, Azure AI Search, Cortex Search), retrieval evaluation, grounding, and citation.
Implement LLM orchestration frameworks (LangChain, LangGraph, LlamaIndex) and apply rigorous prompt engineering with hallucination and accuracy evaluation discipline.
Enterprise Data & Regulated Integration
Deploy applications inside heavily regulated enterprise environments, adhering strictly to RBAC, data residency, audit, and compliance constraints (SOX, GxP).
Connect solutions to governed datasets, semantic models, and core enterprise platforms (SAP, Workday, Coupa, ERP) via secure REST APIs.
Instrument all deployed apps with usage metrics, telemetry, and business outcome tracking from day one.
Strategic Stakeholder Advisory & Pattern Extraction
Work directly with senior Finance leadership (CFO office, FP&A, Controllership) to translate ambiguous operational friction into crisp technical roadmaps.
Extract reusable code components, prompt templates, evaluation harnesses, and retrieval patterns into shared enterprise assets for broader deployment.
Requirements
Experience & Mindset
Experience:
5+ years of hands-on software development and full-stack AI application deployment.
Product-Shaped Builder:
Strong bias for action; comfortable shipping rough prototypes in Week 1 over polished spec documents in Week 4.
Ambiguity Tolerance:
Proven ability to navigate vague business problems without predefined specifications.
Stakeholder Communication:
Outstanding ability to interface directly with senior business leaders without requiring intermediary Business Analysts.
Must-Have Technical Stack
Languages & Frameworks:
Python, FastAPI/Flask, SQL, and UI frameworks (Streamlit, Databricks Apps, React).
AI Platforms:
Direct hands-on experience with
Databricks Genie
.
LLM Engineering:
Production experience with LangChain, LangGraph, LlamaIndex, vector search engines, and prompt evaluation harnesses.
Cloud & DevOps:
Deep working knowledge of Cloud platforms (Azure preferred, AWS/GCP acceptable), modern Git workflows, CI/CD, and basic MLOps awareness (observability, versioning via LangSmith, Datadog, etc.).
Domain Competency (Finance Focus)
Solid foundational understanding of core Finance business processes (FP&A, order-to-cash, procure-to-pay, record-to-report, close cycles) and common financial metrics (OPEX, EBITDA, gross margin, working capital).
Nice-to-Haves & Certifications
Prior FDE, Solutions Engineer, or Field Engineer experience at high-growth AI/Data companies (Palantir, Databricks, OpenAI, Anthropic).
Industry background in Life Sciences, Pharma, Healthcare, or CPG enterprise Finance.
Experience with enterprise Finance software (SAP S/4HANA, Veeva, Workday Adaptive, Oracle Financials).
Certifications: Databricks Certified, or Azure AI Engineer Associate (AI-102).
What This Role Is NOT
Not a pure Data Scientist or ML Researcher:
You will not be training or fine-tuning foundation models from scratch; you are applying existing models to business problems.
Not a non-coding Architect:
This is a 100% hands-on builder role.
Not a pure Backend or MLOps Engineer:
You must be comfortable owning the user-facing interface and direct business interaction.
Location: Foster City, CA
Workplace: Hybrid
Replit is the agentic software creation platform that enables anyone to build applications using natural language. With millions of users worldwide, Replit is democratizing software development by removing traditional barriers to application creation.
About the Role
We are looking for an AI Agent Security Architect to function as the primary technical authority for Replit’s autonomous and AI agent security blueprint. In this critical role, you will design, implement, and maintain the runtime defense systems, guardrail frameworks, and sandboxing architectures that govern AI agents executing code, invoking tools, and reasoning across our platform. You will be a key technical contributor—leading high-impact AI security initiatives and bridging the gap between non-deterministic AI behavior and rigorous cybersecurity controls for both engineering and executive leadership.
What You'll Do
- AI Agent Security Strategy & Technical Execution
- AI Agent Security Blueprint: Define the long-term vision and architectural patterns for securing autonomous agent workflows, Model Context Protocol (MCP) integrations, multi-turn reasoning loops, and multi-agent coordination.
- Runtime Guardrails & Policy Enforcement: Architect and deploy dynamic input/output guardrail systems, semantic firewalls, and real-time intent verification filters to prevent goal hijacking, system prompt leaks, and indirect prompt injections.
- Agent Execution & Tool Sandboxing: Partner with Infrastructure and AppSec teams to design secure, short-lived, micro-isolated environments (e.g., microVMs, WebAssembly, container sandboxes) where agents can dynamically execute code, run shell commands, and interact with host operating systems safely.
- Agentic Threat Modeling & Red Teaming: Conduct specialized threat modeling against non-deterministic systems. Lead automated and manual AI red-teaming initiatives to uncover vulnerabilities in RAG context pipelines, vector stores, and tool-calling interfaces.
- Identity, Delegation & Least Privilege: Architect fine-grained permission models and step-up Human-in-the-Loop (HITL) authorization patterns for agents acting on behalf of users—ensuring agents never exceed the delegated privileges of the end user or misuse API keys.
- Context & Memory Boundary Security: Design strict data isolation and poisoning prevention controls for vector databases, retrieval-augmented generation (RAG) pipelines, and long-term conversation memories across multi-tenant workloads.
- Risk Management & Cross-Functional Enablement
- Maintain the AI Security Source of Truth: Define, document, and maintain authoritative secure design patterns and SDKs for engineering teams building internal or customer-facing AI agent capabilities.
- Contribution to Risk Register: Identify, quantify, and document non-deterministic and agentic security risks (e.g., OWASP Top 10 for LLMs & AI Agents, MITRE ATLAS), ensuring they are accurately represented in the Cybersecurity Risk Register.
- Auditability & Observability: Design tamper-evident logging and telemetry standards for agent reasoning chains, tool execution history, and context assembly to support incident response, forensics, and GRC requirements.
- Compliance & Sales Enablement: Partner with GRC to translate complex AI security controls into enterprise-ready audit documentation (e.g., ISO 42001, NIST AI RMF, EU AI Act readiness) and support Sales on complex enterprise security inquiries regarding AI safety.
Required Skills & Experience
- 6+ years of experience in security engineering or security architecture, with at least 2+ years dedicated specifically to AI/ML security, LLM application security, or securing agentic frameworks.
- Deep understanding of agentic architectures and protocols (e.g., Model Context. Protocol (MCP), LangChain, AutoGen, CrewAI, ReAct frameworks, and vector database technologies).
- Hands-on expertise with threat vectors unique to agentic AI: Indirect Prompt
- Injection, Goal Hijacking, Tool Parameter Tampering, Data Poisoning, and Context Contamination.
- Strong background in applying safety standards and risk frameworks such as OWASP Top 10 for LLMs & AI Agents, NIST AI RMF, and MITRE ATLAS.
- Proficiency in Python, Go, or TypeScript/JavaScript with a proven track record of reviewing code and building functional security tooling or guardrails.
- Experience authoring, maintaining, and evangelizing technical security architecture documentation.
- Exceptional ability to communicate complex non-deterministic AI risks to both deep technical engineers and executive stakeholders.
- Proven track record of contributing to an enterprise Cybersecurity Risk Register.
Bonus Qualifications
- Experience designing runtime sandboxing and isolation technologies (e.g., Firecracker, gVisor, Docker, WebAssembly) for dynamic code execution environments.
Full-Time Employee Benefits Include:
💰 Competitive Salary & Equity
💹 401(k) Program with a 4% match (US Only)
⚕️ Health, Dental, Vision and Life Insurance
🩼 Short Term and Long Term Disability
🚼 Paid Parental, Medical, Caregiver Leave
🏝 Flexible Time Off (FTO) + Holidays
🚗 Commuter Benefits (In-Office & US Only)
📱 Monthly Wellness Stipend
🧑💻 Autonomous Work Environment
🖥 In Office Set-Up Reimbursement (In-Office Only)
🚀 Quarterly Team Gatherings
☕ In Office Amenities (In-Office Only)
Want to learn more about what we are up to?
- Self-driving Company https://replit.com/blog/self-driving-company
- Replit Agent at Scale https://replit.com/blog/evaluating-and-improving-agent-at-scale
- AI Adoption https://replit.com/blog/ai-adoption
- Build Open-Source Apps https://replit.com/build/open-source-app-builder
Interviewing + Culture at Replit
- Operating Principles https://blog.replit.com/operating-principles
- Reasons not to work at Replit https://blog.replit.com/reasons-not-to-join-replit
To achieve our mission of making programming more accessible around the world, we need our team to be representative of the world. We welcome your unique perspective and experiences in shaping this product. We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds.
Location: Stockholm
We're
looking for a Senior AI Engineer to join our Data Center of Excellence (
CoE
) team at Betsson Group.
You'll
design, build, and deploy AI and agentic solutions with
real business
impact, from LLM-driven pipelines to intelligent automation, while helping shape our technical direction. If
you're
passionate about applied AI and enjoy turning emerging research into production-grade systems,
we'd
love to hear from you!
The Wow
You'll
be at the forefront of AI innovation in iGaming - a space where
cutting-edge
technology meets real-time, high-stakes decision-making. As part of our Data
CoE
,
you'll
take ownership of complex AI initiatives end-to-end, from architecture to deployment, working closely with stakeholders to translate business needs into production-grade solutions.
You'll
have the autonomy to solve hard problems independently, the platform to influence how AI is adopted across the business, and the opportunity to share your
expertise
with the wider team.
A taster of what you will be involved with:
Expect to independently design, build, and deploy LLM-powered pipelines and autonomous agents that solve real operational challenges from intelligent automation to advanced retrieval systems.
You'll
work closely with stakeholders to scope and deliver solutions, get hands-on with frameworks like
LangChain
, and help set best practices around
MLOps
and production-grade AI.
You'll
also have opportunities to mentor developers and contribute to internal knowledge-sharing as part of a well-rounded senior role.
What we are looking for:
The ideal candidate must have the following:
BSc/MSc in CS, AI/ML, Data Science, or related field
3+ years industry experience designing, building and deploying Generative AI solutions
Strong proficiency in Python
Hands-on experience with agentic frameworks (LangChain, LangGraph, Strands or equivalent)
Solid foundations in deep learning and foundation models
Experience integrating and orchestrating LLM-based pipelines end-to-end
Ability to lead technical discussions, mentor junior/mid developers, and deliver internal knowledge sessions
Familiarity with LLMOps (CI/CD pipelines, model hosting, evaluation, production monitoring)
Good knowledge of RAG pipelines
The following are preferred but not
required
:
Familiarity with cloud AI services (AWS Bedrock, Azure Open AI)
Experience with fine-tuning LLMs (LoRA, QLoRA)
Experience in building advanced knowledge retrieval systems including Knowledge Graphs and Context Graphs
Previous experience in iGaming industry
What we offer
Much like riding a rollercoaster, sometimes life at Betsson can be lightning fast with twists and turns but always FUN! Then again, what else would you expect from a business 75% millennial and 1700 strong, spread across 7 offices with 900 based out of our Malta HQ alone! We recognise it may not be for the faint-hearted, but if you’re a go-getter, initiator and adrenaline junkie, always striving to push the boundaries and challenge yourself, then you’ll fit right in.
Challenge accepted?
By submitting your application, you understand that your personal data will be processed as set out in our
Privacy Policy
Location: Stockholm
The Wow
We’re looking for a hands-on
AI Engineer
to build best in class applications and solutions at Betsson Group. You’ll develop AI solutions and applications, context systems and AI Agents, maintain and enhance multiple AI system and products, research frontier models and collaborate with product and architecture teams to deliver high-performing services used by millions of players. This is an opportunity to work with bleeding edge technology, build world-class products.
A taster of what you will be involved with:
As an engineer, your focus will be on developing and maintain, modular and reusable systems, explore new technologies and find solutions to enhance our products.
You’ll participate on design discussions, troubleshoot complex problems, and contribute directly to our codebase.
You’ll also play a key part in building a collaborative, high-performing team culture. Whether reviewing code, or supporting other engineers and tech lead..
What we are looking for:
Proven experience developing and delivering production ready AI Applications
Strong technical background in Generative AI, LLMs and Prompt Engineering
Experience building context systems using RAG, Vector Stores and MCP
Experience on developing AI Agents using frameworks like LangGraph and LangChain
Experience mentoring or leading engineers in a technical capacity
What we offer
Much like riding a rollercoaster, sometimes life at Betsson can be lightning fast with twists and turns but always FUN! Then again, what else would you expect from a business 75% millennial and 1700 strong, spread across 7 offices with 900 based out of our Malta HQ alone! We recognise it may not be for the faint-hearted, but if you’re a go-getter, initiator and adrenaline junkie, always striving to push the boundaries and challenge yourself, then you’ll fit right in.
Challenge accepted?
By submitting your application, you understand that your personal data will be processed as set out in our
Privacy Policy
Программа рассчитана на разработчиков с опытом, которые хотят углубиться в AI-разработку и научиться применять AI в своей работе.
Старт курса - осень 2026 года. Длительность - 6 месяцев, за это время участники смогут:
• Освоить работу с GPT, Claude, Cursor, LangChain, RAG и другими AI-технологиями;
• Научиться автоматизировать до 70% рутинных задач команды;
• Создать production-ready AI-продукт в портфолио;
• Научиться проектировать системы с продуманной архитектурой.
Грант TechOrda покрывает 50% стоимости обучения.
Дедлайн подачи заявок — 7 сентября. Количество грантов ограничено, ранние заявки рассматриваются в первую очередь.
Задачи:
— Разрабатывать AI-ассистентов, чат-ботов и AI-агентов.
— Создавать многоэтапные AI-workflow.
— Интегрировать OpenAI, Anthropic и другие LLM API.
— Работать с context, memory, state и tool calling.
— Разрабатывать AI-решения для Telegram, WhatsApp и Web.
— Автоматизировать бизнес-процессы с помощью AI.
— Разрабатывать и улучшать RAG-системы.
— Работать с embeddings, semantic search, hybrid search и reranking.
— Настраивать обработку, chunking и индексацию документов.
— Работать с векторными, SQL- и NoSQL-базами данных.
— Реализовывать цитирование и проверку источников ответов.
— Разрабатывать backend на Python / FastAPI и создавать REST API.
— Работать с Pydantic и асинхронным Python.
— Интегрировать внешние API и базы данных.
— Реализовывать retry, timeout, error handling и logging.
— Контейнеризировать сервисы через Docker.
— Оптимизировать промпты и системные инструкции.
— Создавать тестовые сценарии и evals.
— Выявлять галлюцинации и проблемы с контекстом.
— Проводить регрессионное тестирование AI-функциональности.
— Сравнивать модели по качеству, скорости и стоимости.
— Контролировать token usage, latency, ошибки и расходы на LLM.
— Анализировать реальные диалоги пользователей и улучшать AI-системы.
Требования:
— От 2 лет коммерческого опыта в AI Engineering, Backend, ML Engineering или смежной технической роли.
— Уверенный Python.
— Практический опыт работы с FastAPI.
— Опыт работы с REST API, Pydantic и асинхронным Python.
— Опыт работы с Docker и Git.
— Практический опыт интеграции OpenAI, Anthropic или других LLM API.
— Опыт работы с LangChain, LangGraph или аналогичными инструментами.
— Понимание RAG, embeddings и semantic search.
— Опыт разработки AI-ассистентов, чат-ботов или AI-агентов.
— Опыт работы с SQL и/или NoSQL.
— Понимание ограничений LLM: галлюцинации, context window, latency и стоимость.
— Умение писать структурированный и поддерживаемый код.
— Способность самостоятельно разбираться в задачах и предлагать технические решения.
— Английский язык.
Будет большим плюсом:
— Qdrant, Pinecone, pgvector, Weaviate, Milvus или другие vector DB.
— Production-ready RAG.
— AI Agents и tool calling.
— Structured Outputs, JSON Schema и Function Calling.
— LLM evaluation / automated evals.
— LangSmith, Langfuse, Arize Phoenix или аналогичные инструменты.
— PostgreSQL, Redis, Elasticsearch / OpenSearch.
— CI/CD.
— AWS, GCP, Azure.
— OCR и обработка документов.
— Опыт разработки multi-tenant SaaS.
— Понимание информационной безопасности и защиты данных.
AI-инструменты в разработке:
— Claude Code
— Cursor
— GitHub Copilot
— Codex и аналоги
Ожидания на первые 3 месяца:
— Разобраться в существующей AI- и backend-архитектуре.
— Самостоятельно развивать LLM-функциональность.
— Реализовать минимум одну значимую AI-функцию от идеи до production.
— Улучшить качество, стабильность, скорость или стоимость существующих AI-сценариев.
— Настроить базовое тестирование и оценку качества AI-ответов.
— Улучшить логирование и мониторинг ключевых AI-сценариев.
Условия:
— Полная или частичная занятость.
— Формат: удалённо / гибрид.
— График: 5/2 и другие варианты.
— Рабочий день 8 часов.
— Оформление по трудовому договору.
— Остальные условия обсуждаются индивидуально с финальным кандидатом.
— Работа над production-ready AI-системами, а не только демонстрационными решениями.
Location: Atlanta, GA
Workplace: OnSite
ABOUT US
At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale.
We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry.
More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale.
ABOUT THE TEAM
The Deployed Engineering team works directly with companies building and running AI agents in production, helping turn ideas and prototypes into systems teams can rely on.
This is a hands-on, highly technical team that partners closely with customer engineers across the full lifecycle, from pre-sales evaluations to post-deployment advisory work. The focus is on achieving the technical win, co-designing agent architectures, and helping customers operate agents reliably at scale using the LangChain suite.
Deployed Engineers sit at the intersection of engineering, product, and go-to-market, shaping how LangChain is adopted in the field and feeding real-world insights back into the platform.
ABOUT THE ROLE
The Deployed Engineer…You’ll work on some of the hardest problems in applied AI — not demos, not research, but systems that real teams depend on in production. The feedback loop is fast, the impact is visible, and the work you do directly shapes how AI agents are built in the real world.
WHAT YOU’LL DO
- Co-architect and co-build production AI agents with customer engineering teams
- Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations
- Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows
- Advise customers post-sale on architecture, best practices, and roadmap-level decisions
- Run technical demos, trainings, and workshops for developer audiences
- Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers
- Occasionally contribute code upstream when it meaningfully improves customer outcomes
WHAT YOU’LL BRING
- 3+ years in a relevant technical role (software engineering, customer engineering, solutions engineering, founding/product engineering), ideally in a startup or scale-up
- Strong Python, JavaScript and systems fundamentals
- Have designed agent-based or LLM-powered applications beyond simple API calls, including multi-step workflows, orchestration, and failure handling
- Are comfortable working directly with customers during POCs, architecture reviews, and technical evaluations
- Can explain technical tradeoffs clearly and build trust with developer audiences
- Take responsibility for outcomes, not just recommendations
- Have a bias toward action and enjoy figuring things out as you go
- Are excited about operating AI agents in production, not just building demos
NICE TO HAVE’S:
- You’ve deployed AI agents in production, especially using LangChain, LangGraph, or similar frameworks
- Worked with LLM evaluation, observability, or guardrails
- Have experience with cloud environments (AWS, GCP, Azure), containers, and basic Kubernetes concepts
- Have shipped and operated production software and are comfortable owning systems under real-world constraints
COMPENSATION
Annual OTE range: $150,000–$250,000 USD
COMPENSATION PHILOSOPHY:
We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks.
We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations.
BENEFITS
Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.
Location: Remote - US; Boston, MA; New York, NY
Workplace: Remote
ABOUT US
At LangChain, our mission is to enable every company to own its intelligence. We believe agents will transform how companies operate, and we're building the foundation to make that future possible. Our open-source harnesses and LangSmith platform give developers and enterprises the infrastructure to run, improve, and govern agents at scale.
We've raised $460M to date, with our most recent funding round valuing the company at $3B, backed by leading investors including Sequoia, Benchmark, IVP, and CapitalG. As we continue to expand our platform and tackle some of the hardest challenges in agent engineering, every team member has the opportunity to make a meaningful impact and help shape both our technology and the future of the industry.
More than 7,000 organizations rely on LangSmith, from fast-growing startups to Fortune 500 enterprises, including NVIDIA, Bridgewater, LinkedIn, Workday, and Rippling. We're just getting started on making high-performing agents easier to build and manage at scale.
ABOUT THE ROLE:
This role can be based remotely in the U.S. Ideally SF, NYC, or Boston.
LangChain is building the infrastructure that powers the next generation of AI applications. Our tools are used by thousands of developers and teams to build, debug, and ship production-grade LLM-powered products.
As our product surface area grows, so does the need for a cohesive, scalable, and high-quality UI foundation. That’s where Design Systems comes in. We’re looking for a Design Systems Engineer to help build, own, and evolve LangChain’s component library—the foundation that our frontend teams rely on to ship fast without sacrificing quality, accessibility, or consistency.
This role sits at the intersection of Design and Engineering. You’ll work closely with designers, product engineers, and platform teams to turn design intent into robust, reusable components and tooling that scale across the product.
You’ll have ownership, influence, and your work will be directly consumed across LangChain’s products.
WHAT YOU'LL DO:
- Build out LangChain’s component library
- Design and implement reusable, composable React components that are flexible, accessible, and production-ready
- Partner closely with designers to translate design systems, tokens, and interaction patterns into code
- Build reliable UI quality systems
- Develop design system tooling and improve workflows around theming, tokens, versioning, testing, and component discovery (e.g. Storybook)
- Make thoughtful architectural decisions that enable accessibility, performance, consistency, and maintainability across LangChain’s UI stack
- Improve UI build and test workflows
- Optimize build performance, bundling strategy, and package versioning for the component library
- Maintain tooling for testing, dependency management of the UI app
WHAT YOU'LL BRING:
- Extensive experience building modular, composable, and well-documented component libraries and/or design systems
- Deep knowledge of React, TypeScript, HTML, and CSS, familiarity with: styling approaches (CSS Modules, CSS-in-JS, Tailwind, Sass, etc.) and design system toolings (Storybook, Chromatic, tokens pipelines, etc.)
- Experience collaborating closely with designers and non-engineering partners
- Strong attention to detail and a high bar for craft and genuine passion for accessibility and inclusive design
- Familiarity with testing UI components and visual regression testing
Annual salary range: $180,000 - $240,000
COMPENSATION PHILOSOPHY:
We offer competitive compensation that includes base salary, variable compensation for relevant roles, meaningful equity, benefits, and perks.
We are hiring for this role across a range of scope, and the compensation band reflects that. Your offer depends on what we learn about your impact, craft, leadership, and communication during the interview process. We will tell you early where you’re tracking and talk comp with you before offer stage. Team members in the EU, UK, and APAC receive locally competitive benefits aligned with regional norms and regulations.
BENEFITS
Benefits include medical, dental, and vision coverage, flexible vacation, a 401(k) plan, meals on in-office days in the US and more.
Задачи:
- Поддерживать текущие алгоритмические решения
- Проектировать и внедрять AI-агентов (LLM, GPT-like модели) в различные процессы
- Адаптировать и дообучать LLM (например, LLaMA, Mistral, YaLM 2) под специфику продукта
- Разрабатывать RAG-пайплайн для поиска и обработки данных
- Настраивать векторные БД (Pinecone/Weaviate/Qdrant) и оптимизировать кэширование
Требования:
- Знание современных AI-фреймворков и библиотек (LangChain, LlamaIndex и других)
- Уверенное владение Python и PyTorch/TensorFlow
- Практический опыт работы с API, Docker/Kubernetes и базами данных
- Опыт внедрения RAG
Location: Wrocław, Poland
What is Box?
Box (NYSE:BOX) is the leader in Intelligent Content Management. Our platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. We help companies thrive in the new AI-first era of business. Founded in 2005, Box simplifies work for leading global organizations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia.
By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It’s the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations. With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift.
Why Box needs you:
We are looking for a Machine Learning Engineer to design, build, and operate reliable machine learning systems that deliver meaningful product impact at enterprise scale. In this role, you will combine strong software engineering and applied machine learning skills to take solutions from idea through production. You will work closely with engineering, product, data, and infrastructure partners to develop scalable ML and agentic capabilities, improve model quality, and ensure that systems are secure, observable, and dependable for Box customers.
The Metadata Extraction team builds AI-powered capabilities that transform unstructured enterprise content into structured, usable metadata. The team works on the platform and machine learning layer of document understanding, developing and operationalizing technologies such as OCR and LLM-based extraction agents. These capabilities help understand document layout, tables, handwriting, semantic content, and other complex information within enterprise documents. As a Machine Learning Engineer, you will build reliable, scalable systems that support the development, evaluation, deployment, and operation of these AI capabilities. You will collaborate with engineers, product partners, and adjacent platform teams to bring document intelligence features to Box customers at enterprise scale.
What you'll do:
Design, build, deploy, and maintain production LLM agents, machine learning models and services.
Develop robust data preparation pipelines, prompt and tool design, and agentic workflows.
Write high-quality, maintainable, and well-tested Python code for production systems.
Partner with Product, Software Engineering, Data, and Infrastructure teams to translate customer and business needs into ML-powered capabilities.
Evaluate and improve agents quality using appropriate offline and online metrics, eval sets, experiments, and feedback loops.
Monitor production ML systems for quality, reliability, latency, cost, and data or model drift; investigate and resolve issues as needed.
Contribute to the architecture of scalable services and pipelines that support real-time and batch ML workloads.
Create clear technical designs, documentation, tests, and operational runbooks.
Share knowledge, contribute to engineering best practices, and help raise the bar for code quality and operational excellence.
Participate in an on-call rotation and help triage production issues when on call.
Who you are:
We are an AI-first company. This means you approach your work with a growth mindset and find ways to leverage AI to help make faster, smarter decisions that will 10X your impact at Box.
You have 3+ years of experience building, deploying, or operating production software, data, or machine learning systems.
You have strong programming skills in
Python
and are comfortable writing production-grade, readable, testable, and maintainable code.
You have practical experience with the LLM based agents lifecycle: data and eval-set preparation, prompting and tool schemas, RAG, evaluation, deployment and monitoring.
You have experience with LLM application frameworks and libraries, such as LangChain, LangGraph, PyTorch, Pydantic, Pandas, or NumPy, and are comfortable calling LLM APIs (e.g. Gemini, Claude, OpenAI).
You understand core machine learning concepts and can make pragmatic trade-offs between model performance, reliability, latency, scalability, and cost.
You have experience building APIs, services, pipelines, or distributed systems in a cloud environment.
You are familiar with CI/CD, observability, testing, and production support practices.
You communicate clearly with both technical and non-technical partners.
You have an ownership mindset, enjoy solving ambiguous problems, and work collaboratively across teams.
Nice to have:
Experience with MLOps tooling, model monitoring, feature stores, workflow orchestration, or experiment tracking.
Experience with generative AI in production: multi-provider gateways, semantic search, or structured extraction.
Experience with multimodal intelligent document processing workflows.
Experience with a major cloud platform, including GCP, AWS, or Azure, and technologies such as Kubernetes and Terraform.
Experience with SQL and large-scale analytics (e.g. BigQuery) for quality, cost, and incident investigation.
Experience in enterprise software, security, search and retrieval, or content intelligence.
Experience mentoring engineers or contributing to technical direction.
Methodology
Agile management - Scrum
Issue tracking tool -Jira
Knowledge repository - GitHubEnterprise
Code reviews - GitHubEntreprise
Version control system - GIT
BENEFITS
Check out the
overview of Life at Box
which include general perks and benefits.
Box lives its values, with community and in-person collaboration being a core part of our culture. Boxers are expected to work from their assigned office a minimum of 3 days per week. Your Recruiter will share more about how we work and company culture during the hiring process.
At Box, we believe unique and diverse experiences benefit our culture, our products, our customers, our company, and our world. We aim to recruit a passionate, high-performing workforce that reflects the world we live in.
If you are head-over-heels about this role but unsure if you meet all the requirements, we encourage you to apply!
EQUAL OPPORTUNITY
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, disability, and any other protected ground of discrimination under applicable human rights legislation.
For details on how we protect your information when you apply, please see our
Personnel Privacy Notice
.
For more details on how Box Poland protects your information, please see our
Supplemental Personnel and Candidate Privacy Notice
.
NOTICE OF USE OF AI
At Box, we believe in the power of AI to improve the recruiting process while keeping people at the heart of every decision. For more information on the use of AI in our hiring process, please visit our webpage
here
.
#LI-hybrid #LI-OB1
Location: Munich; Neuss; Berlin
Workplace: Hybrid
ROLE HIGHLIGHTS:
- Role: Forward Deployed AI Engineer
- Department: Forward Deployed Engineering
- Location: Germany
- Reports to: VP, Forward Deployed Engineering
- Working type: Hybrid (On-site at customer locations approx. 50% of time)
- German security clearance (Sicherheitsüberprüfung Ü1/Ü2/Ü3)
- Must be eligible and able to attain relevant national and NATO security clearances
- Must be based in-country and native level speaker of the country you're deploying to.
THE MISSION OF THE FORWARD DEPLOYED AI ENGINEER
In this role, you'll be the hands-on AI builder defense and intelligence customers rely on. You’ll deploy ICEYE’s intelligence platform into secure (often air-gapped) environments and rapidly ship AI-powered workflows on top of it—everything from Docker-based deployments to RAG systems fusing SAR imagery with intelligence reports. This is a ship-code-daily role: turning raw satellite data into mission-critical tools analysts use to protect nations.
WHO WE ARE
ICEYE is the world leader in sovereign intelligence from space. We deliver persistent monitoring capabilities to detect and respond to changes in any location on Earth.
ICEYE owns the world's largest and most advanced SAR (synthetic aperture radar) satellite constellation. To our customers we provide intelligence with unmatched quality, latency and revisit times, in any weather, day or night. To governments who choose to operate their own constellation we provide this proven capability as a sovereign system.
ICEYE-built constellations serve customers in defence and intelligence, environmental monitoring, insurance and emergency management. We enable fast decisions that contribute to a safer future.
Founded and headquartered in Finland, ICEYE operates globally with over 1000 employees across Europe, North America, the Middle East, and Asia-Pacific.
DAY TO DAY RESPONSIBILITIES:
PLATFORM DEPLOYMENT & INTEGRATION
- Deploy ICEYE's intelligence platform into customer secure environments (on-premise, air-gapped networks, classified data centers).
- Build and maintain containerized applications (Docker) and manage deployments in customer Kubernetes clusters.
- Develop APIs and data pipelines connecting ICEYE systems to customer C4ISR infrastructure and intelligence tools.
- Troubleshoot deployment issues in disconnected environments without external internet access.
AI SOLUTION DEVELOPMENT & CUSTOMIZATION
- Build Retrieval-Augmented Generation (RAG) systems that combine ICEYE SAR data with customer intelligence documents for context-aware analysis.
- Develop AI agents and workflows using frameworks like LangChain, LangGraph, or similar tools to automate analytical tasks.
- Implement prompt engineering strategies and conversational AI interfaces that enable analysts to query satellite data using natural language.
- Create Model Context Protocol (MCP) integrations connecting AI systems with customer data sources.
DATA FUSION & INTELLIGENCE WORKFLOWS
- Build data fusion pipelines combining SAR imagery with additional intelligence sources (AIS, RF signals, optical imagery, SIGINT).
- Develop automated workflows that correlate and cross-reference information across different data modalities.
- Create lightweight tools, dashboards, and alerting systems tailored to customer operational needs.
CUSTOMER ENGAGEMENT & RAPID PROTOTYPING
- Work embedded with intelligence analysts, data scientists, and military operators to understand mission-critical problems.
- Rapidly prototype AI-powered solutions (code first, polish later) that address immediate operational requirements.
- Conduct hands-on training sessions on platform capabilities and AI workflow usage.
- Gather customer feedback and translate it into technical requirements for ICEYE's product and engineering teams.
WHAT WE ARE LOOKING FOR
MUST HAVE SKILLS:
- 3+ years hands-on software engineering building and deploying production systems.
- Strong Python programming skills (required) plus one of: Go, TypeScript, or Rust.
- Hands-on experience with LLMs, prompt engineering, and RAG systems.
- Working knowledge of agent frameworks (LangChain, LangGraph, pydantic-ai) and vector databases.
- Experience with Docker and deploying in secure / isolated / air-gapped environments.
- Practical experience with APIs, data pipelines, and integrating systems in production,
- Familiarity with Linux and basic Kubernetes operations.
- Builder mindset: ship fast, iterate with feedback, and prioritize working solutions over perfect architecture.
- Strong communication + customer engagement, able to work embedded with intelligence analysts / military operators.
- Strong spoken and written English, able to explain technical concepts to non-technical stakeholders.
- Willingness to travel 40–60% to customer sites within the region.
NICE TO HAVE SKILLS:
- Experience in defense, intelligence, or government operational environments.
- Deploying to on-prem data centers and/or air-gapped networks.
- Familiarity with geospatial data, satellite imagery, or remote sensing.
- Experience with MCP integrations, multi-agent systems, or agentic workflows.
- Deeper Kubernetes, IaC (Terraform/Ansible), and/or offline CI/CD.
- Understanding of intelligence analysis workflows and defense/government requirements.
- Experience with data fusion, multi-source intelligence integration, or GIS tools (ArcGIS, QGIS).
- Background in solution engineering, technical consulting, or customer success.
- Experience in defense tech (e.g., Palantir, Anduril, Scale AI, similar startups).
APPLICATION PROCESS
- Talent Acquisition Interview
- Take home task
- Hiring Manager Interview
- Hiring Team Interview
- Offer Decision
WORKING AT ICEYE
At ICEYE, you’ll join a diverse and highly engaged team united by the ambition to make the impossible possible. As a global scale-up, we combine speed and ambition with the opportunity to take real ownership from day one. Your growth, wellbeing, and success are a priority, with continuous professional development, training opportunities, and a culture where collaboration is how we win.
HOW WE WORK (OUR VALUES)
Make the impossible possible: We set ambitious goals and stay calm under pressure. We bring grit, optimism, and ownership when things get hard, and we keep moving until we find a way.
- Be curious: Go deep, ask questions, listen carefully, and think critically. Understand the “why” behind decisions.
- See the big picture: Stay close to what’s happening across the company so you can make better decisions. Consider how your work affects others.
- Drive effective teamwork: Create psychological safety, invite different perspectives, and build inclusive teams. There are no bad questions.
- Act as one team: We win together. We match tasks to the right owner and stay agile as priorities shift.
- Have fun: What we do matters—and it should be enjoyable. Celebrate progress, take pride in results, and share the wins.
BENEFITS
Our benefits are designed to support your health and wellbeing—at work and beyond. We keep improving them based on employee feedback, and offerings vary by location. Talent Acquisition will confirm what applies for this role and location during the process.
OUR COMMITMENT TO DIVERSITY, EQUITY AND INCLUSION
We want ICEYE to be a place where people can be themselves and do great work. Different backgrounds and perspectives make us stronger, which is why we work to create an environment where people feel included, respected, and able to speak up. Whatever your background, we want you to bring your authentic self to the table.
We’re committed to fair, inclusive hiring and equal opportunity. Everyone is welcome to apply. If you need any adjustments or support during the recruitment process, tell us—we’ll do our best to help.
Apply now to start your ICEYE journey, and help us continue to make the impossible possible together. Read more about ICEYE and working with us at iceye.com https://www.iceye.com
Задачи:
— Проектирование и разработка продвинутых AI-workflows и агентных архитектур
— Разработка и оптимизация RAG-пайплайнов
— Создание качественных промптов и проведение экспериментов со стратегиями prompt engineering
— Работа с LangChain и другими LLMOps-инструментами для оптимизации производительности чат-бота
— Анализ поведения пользователей для повышения точности и естественности AI-системы
— Тесное взаимодействие с инженерной и продуктовой командами при тестировании и внедрении улучшений
— Отслеживание новых тенденций в области LLM и conversational AI
— Развитие GPT-powered чат-бота компании до более высокого уровня качества и персонализации
Требования:
— Подтвержденный опыт работы с GPT или другими большими языковыми моделями
— Практический опыт создания AI-агентов и agentic workflows
— Глубокое понимание RAG и опыт его применения в реальных проектах
— Уверенное знание Python
— Знакомство с векторными базами данных
— Опыт работы с LangChain
— Понимание LLMOps best practices
— Отличный английский для написания промптов и коммуникации с командой
— Аналитический склад мышления
— Проактивный подход к экспериментам и поиску решений
— Интерес к AI и постоянному профессиональному развитию
Условия:
— Полная занятость
— Удалённый формат работы
— График 5/2
— Вечерние или ночные смены
— Рабочий день 8 часов
— Выплаты один раз в месяц
— Оформление по договору ГПХ с самозанятым
— Заработная плата $2 000–4 500 в месяц до вычета налогов
— Работа над AI-продуктом в сфере iGaming
— Возможность развивать GPT-powered chatbot и современные conversational AI решения
Location: San Francisco
Workplace: Hybrid
ABOUT BASETEN
Baseten powers mission-critical inference for the world's most dynamic AI companies, like Cursor, Notion, OpenEvidence, Abridge, Clay, Gamma, and Writer. By uniting applied AI research, flexible infrastructure, and seamless developer tooling, we enable companies operating at the frontier of AI to bring cutting-edge models into production. We're growing quickly and recently raised our $1.5B Series F https://www.baseten.co/blog/announcing-our-series-f/, led by Altimeter Capital, Conviction Partners, and Spark Capital. Join us and help build the platform engineers turn to ship AI products.
THE ROLE
Are you the person on your team who builds the agent everyone else ends up using? We're looking for an AI Engineer to join our Training Product team and do that at Baseten. You'll build AI-driven product features for the customers training and post-training frontier models on our platform, and you'll raise the ceiling on how Baseten itself uses AI internally, turning manual workflows into agentic ones that make every other team faster.
You'll work directly with our research engineers to scope and build products, taking ideas from a research loop that already works internally to something customers can run themselves. This is a hands-on role with real autonomy. You'll pick the problems worth solving, build the harnesses, execution flows, and guardrails that make AI systems reliable, and own the results. If you've been shipping agents and want that to be the job, let's talk.
EXAMPLE INITIATIVES
Take a look at these blog posts written by members of our team:
- Baseten Training: an autoresearch substrate https://www.baseten.co/blog/baseten-training-an-autoresearch-substrate/
- Introducing Baseten Loops https://www.baseten.co/blog/introducing-the-baseten-loops-sdk/
- Harnesses are everything. Here's how to optimize yours. https://www.baseten.co/blog/harnesses-are-everything-heres-how-to-optimize-yours/
- Building with NVIDIA Nemotron 3 Ultra and LangChain Deep Agents Code on Baseten https://www.baseten.co/blog/nvidia-nemotron-3-ultra-and-langchain-deep-agents-on-baseten/
RESPONSIBILITIES
- Build and ship agentic product experiences, including chat-style and assistant-like interfaces, from prototype to GA.
- Design the harnesses, execution flows, and guardrails that make AI systems reliable in production.
- Build internal automation and AI tooling that measurably increases the velocity of engineering, research, and go-to-market teams.
- Partner with research engineers to scope product opportunities out of internal research workflows and turn them into customer-facing features.
- Define and instrument evals so you know whether a change actually improved output quality.
- Work throughout the stack (API layer, backend, agent orchestration, frontend) to implement features end to end.
- Use Baseten's own training and inference products yourself to develop intuition around customer workflows.
- Identify where AI can replace manual process across the company and build the thing rather than write the proposal.
- Fix bugs and resolve customer issues with urgency.
REQUIREMENTS
- 5+ years of experience building and shipping software applications.
- Demonstrated experience building AI or LLM-powered products, agents, or agentic workflows that real users depend on.
- Strong software engineering fundamentals and the ability to clear a real technical bar, not just prompt well.
- Ability to build accurate mental models of how systems work under the hood, including the models and harnesses you're building on.
- Proficiency in Python, with fluency in at least one other language.
- Comfort working autonomously in a fast-moving environment with limited structure.
- Ability to move between customer-facing product work and internal tooling and automation.
- Strong communication skills, with the ability to bridge technical depth and business needs.
NICE TO HAVE
- Experience as a founding engineer or early employee at a startup.
- Experience building evals, agent observability, or tooling for non-deterministic systems.
- Familiarity with agent frameworks and harnesses (LangChain, Claude Code, Codex, OpenCode, MCP).
- Experience with model development methods like supervised fine-tuning, reinforcement learning, synthetic data generation, LoRA, and full fine-tunes.
- Frontend fluency.
BENEFITS
- Competitive compensation, including meaningful equity
- (U.S. only) 100% coverage of medical, dental, and vision insurance for employee and dependents
- Flexible PTO policy including company wide Winter Break (our offices are closed from Christmas Eve to New Year's Day!)
- Paid parental leave
- Fertility and family-building stipend through Carrot
- (U.S. only) Company-facilitated 401(k)
- Exposure to a variety of ML startups, offering unparalleled learning and networking opportunities.
Apply now to embark on a rewarding journey in shaping the future of AI! If you are a motivated individual with a passion for machine learning and a desire to be part of a collaborative and forward-thinking team, we would love to hear from you.
At Baseten, we are committed to fostering a diverse and inclusive workplace. We provide equal employment opportunities to all employees and applicants without regard to race, color, religion, gender, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, or veteran status.
We are an Equal Opportunity Employer and will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance, where applicable).
Задачи:
- Разработка и поддержка Python-скриптов и микросервисов для интеграции AI-инструментов в банковские процессы
- Исследование и пилотирование AI-платформ (OpenClaw, Claude Code, LangChain и аналоги)
- Участие в проектировании бэкенд-компонентов для взаимодействия с LLM
- Проведение экспериментов: тестирование гипотез, сравнение моделей и инструментов
- Участие в Code Review
Требования:
- Базовое знание Python (ООП, структуры данных, модули)
- Понимание Git (коммиты, ветки, PR)
- Знакомство с FastAPI (учебные проекты) и pytest
- Понимание SQL (простые запросы) и принципов БД
Условия:
- Стажировка 3 месяца с возможностью продления или перехода в штат
Location: Remote, Canada; Remote, Poland; Remote, United Kingdom
GitLab is the intelligent orchestration platform for DevSecOps. GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation. More than 50 million registered users and more than 50% of the Fortune 100* trust GitLab to ship better, more secure software faster.
The same principles built into our products are reflected in how our team works: we embrace AI as a core productivity multiplier, with all team members expected to incorporate AI into their daily workflows to drive efficiency, innovation, and impact. GitLab is where careers accelerate, innovation flourishes, and every voice is valued. Our high-performance culture is driven by our values and continuous knowledge exchange, enabling our team members to reach their full potential while collaborating with industry leaders to solve complex problems.
Co-create the future with us
as we build technology that transforms how the world develops software.
*
Fortune 500® is a registered trademark of Fortune Media IP Limited, used under license. Claim based on GitLab data. Fortune 100 refers to the top 20% ranked companies in the 2025 Fortune 500 list, published in June 2025. Fortune and Fortune Media IP Limited are not affiliated with, and do not endorse products or services of GitLab.
An overview of this role
As an Intermediate Backend Engineer (Python) in GitLab's Agent Foundations stage, you'll go beyond using AI tools. You'll help build the foundations for GitLab's agentic AI experiences: the platform that lets AI agents work across the whole software delivery lifecycle, not just write code. That covers the reusable building blocks agents and flows are made from as well as our core harness, Duo Developer.
Most of your work will be backend engineering in Python, using libraries such as LangGraph to build secure, reliable and scalable features in our AI Gateway. You'll also work across the stack where needed. You'll work closely with engineers across the stage and other AI Engineering teams. This is a high-visibility area where GitLab's core platform meets its AI strategy.
What you'll do
Develop, ship and maintain features for the Duo Agent Platform in the Duo Workflow Service, the AI Gateway and, where needed, the GitLab monolith. Keep them secure, well-tested and performant.
Build and improve agentic flows and their reusable parts with LangChain and LangGraph, including the Flow Registry.
Work with Product Management, UX, frontend, backend and AI specialists to refine requirements and ship improvements in small iterations.
Design, implement and review APIs (including GraphQL and REST) and the backend logic behind them, with reliable, scalable and clear contracts for clients such as the GitLab monolith, IDE extensions and Duo CLI.
Improve and extend automated testing (pytest and other frameworks), including evaluations of agent and flow behavior, to strengthen quality and give developers faster feedback.
Share standards, patterns and learnings with other engineers to raise the bar for responsible AI integration and evidence-driven engineering.
Join Tier 2
on-call rotations
to troubleshoot production issues, contribute to root cause analysis, and improve observability and resiliency.
Keep an eye on the competitive landscape to help keep GitLab's agent platform best-in-class as more work becomes agentic.
Take part in on-call or operational support where your team’s ownership model requires it.
What you'll bring
Experience building and maintaining production Python backend applications, including APIs, asynchronous or background processing, and data models.
Experience with LLM application frameworks such as LangGraph, or designing and shipping AI-powered backend features or harnesses.
Skill in designing or extending APIs (REST, GraphQL or gRPC) with attention to scalability, maintainability and backward compatibility.
Comfort working across the stack in a mature codebase. Familiarity with Ruby on Rails and/or JavaScript and Vue is a plus.
Experience finding and fixing performance bottlenecks in applications.
Clear written and verbal communication, including technical proposals and documentation, in a remote, async, cross-functional team.
Openness to learning and to applying skills from related technologies or fields.
About the team
The
Agent Foundations stage
builds the foundations for GitLab's agentic AI experiences. We provide the platform, catalog and execution features that help teams deliver resilient agents and flows to customers. The stage has three groups: Agent Developer, AI Catalog and Agent Execution. Between them, they own the GitLab Duo Developer foundational flow and the Flow Registry; the AI Catalog, including custom agents, custom flows and external agent integrations; and agent execution, agent tools and agent observability.
Our engineers work async-first across EMEA, AMER, and India timezones. We work with teams across AI engineering and the rest of GitLab so the Duo Agent Platform can power many of GitLab's AI features.
How GitLab Supports Full-Time Employees
Benefits to support your health, finances, and well-being
Flexible Paid Time Off
Team Member Resource Groups
Equity Compensation & Employee Stock Purchase Plan
Growth and Development Fund
Parental Leave
Please note that we welcome interest from candidates with varying levels of experience; many successful candidates do not meet every single requirement. Additionally, studies have shown that people from
underrepresented groups
are less likely to apply to a job unless they meet every single qualification. If you're excited about this role, please apply and allow our recruiters to assess your application.
Country Hiring Guidelines:
GitLab hires new team members in countries around the world. All of our roles are remote, however some roles may carry specific location-based eligibility requirements. Our Talent Acquisition team can help answer any questions about location after starting the recruiting process.
Privacy Policy:
Please review our
Recruitment Privacy Policy.
Your privacy is important to us.
GitLab is proud to be an equal opportunity workplace and is an affirmative action employer. GitLab’s policies and practices relating to recruitment, employment, career development and advancement, promotion, and retirement are based solely on merit, regardless of race, color, religion, ancestry, sex (including pregnancy, lactation, sexual orientation, gender identity, or gender expression), national origin, age, citizenship, marital status, mental or physical disability, genetic information (including family medical history), discharge status from the military, protected veteran status (which includes disabled veterans, recently separated veterans, active duty wartime or campaign badge veterans, and Armed Forces service medal veterans), or any other basis protected by law. GitLab will not tolerate discrimination or harassment based on any of these characteristics. See also
GitLab’s EEO Policy
and
EEO is the Law
. If you have a disability or special need that requires
accommodation
, please let us know during the
recruiting process
.
About Us & The Team
Til-Qazyna is a national scientific and practical center dedicated to building the core digital and AI infrastructure for the Kazakh language. We develop foundational language resources, linguistic tools, and open-source models, including custom text corpora, morphological analyzers, RAG pipelines, and fine-tuned Large Language Models.
We are looking for an AI Engineer to focus exclusively on natural language processing, corpus engineering, and LLM fine-tuning.
Key Responsibilities
• LLM Adaptation & Fine-Tuning: Fine-tune, evaluate, and benchmark open-source LLMs (Llama, Mistral, Qwen, Gemma) for Kazakh language understanding and generation using parameter-efficient methods (LoRA, QLoRA, PEFT).
• NLP & Linguistic Tooling: Develop, maintain, and optimize rule-based and neural NLP components, including morphological analyzers, lemmatizers, tokenizers, and terminology parsers.
• Corpus & Data Pipelines: Design automated pipelines for large-scale text crawling, data cleaning, deduplication, synthetic data generation, and dataset curation.
• Inference & Deployment: Package NLP models and LLMs into containerized microservices (Docker, FastAPI) and optimize inference using modern serving frameworks (vLLM, Ollama, TensorRT-LLM, ONNX).
Requirements
• 1+ years of hands-on experience as NLP / AI / Machine Learning engineer.
• Solid understanding of the Transformer architecture, attention mechanisms, embeddings, and tokenization.
• Experience with core ML/NLP libraries: PyTorch, Hugging Face (transformers, datasets, accelerate, peft), Scikit-learn.
• Experience building data preprocessing pipelines and working with structured/unstructured text data.
• Practical knowledge of Docker, Linux, and Git.
• Degree in Computer Science, Data Science, Applied Mathematics.
Nice to Have
• Hands-on experience with modern LLM serving and optimization engines (vLLM, TGI, llama.cpp, quantization techniques like AWQ/GPTQ).
• Experience with RAG stacks (LangChain, LlamaIndex, ChromaDB, Qdrant, FAISS).
• Understanding of the morphological, agglutinative, or grammatical characteristics of the Kazakh language.
• Familiarity with alignment techniques (DPO, RLHF) or synthetic data generation.
What We Offer
• Direct impact on sovereign AI development and language technology used at scale.
• Official employment in full compliance with the Labor Code of the Republic of Kazakhstan.
• Access to high-performance GPU compute clusters for model training and experimentation.
ОфисAstana
ГибридСША; 💹 401(k) Program with a 4% match (US Only); 🚗 Commuter Benefits (In-Office & US Only); We encourage people from all kinds of backgrounds to apply, including and especially candidates from underrepresented and non-traditional backgrounds
seniorStockholm
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