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Location: USA Workplace: Remote Solvd Inc. is a rapidly growing AI-native consulting and technology services firm delivering enterprise transformation across cloud, data, software engineering, and artificial intelligence. We work with industry-leading organizations to design, build, and operationalize technology solutions that drive measurable business outcomes. Following the acquisition of Tooploox, a premier AI and product development company, Solvd now offers true end-to-end delivery—from strategic advisory and solution design to custom AI development and enterprise-scale implementation. Our capability centers combine deep technical expertise, proven delivery methodologies, and sector-specific knowledge to address complex business challenges quickly and effectively. We are looking for a Senior / Staff Data Engineer to build what the Agent Assist provider calls: the Bedrock Knowledge Base configuration, the OpenSearch Serverless collection, the versioned retrieval API, and the quality dashboard. You’ll own the retrieval workstream end to end, paired with the ingestion engineer, with technical direction from the Solution Architect. This role carries the most technical risk in the engagement — entitlement filtering is a severity-one commitment. The mission: ship a retrieval API that returns the right passages, with citations, only to callers entitled to see them, at a latency and cost the client accepts, and that the client can version and operate after week 10. WHAT YOU’LL DO - Stand up the Bedrock Knowledge Base and OpenSearch Serverless vector collection in the client’s account from infrastructure code, with the embedding model, chunking strategy, and metadata fields the architect specified. - Implement hybrid retrieval — vector plus keyword — with metadata filtering by taxonomy, source, status, and review date; tune top-k, score thresholds, and reranking against the golden question suite. - Build the retrieval API on API Gateway and Lambda per the Integration Blueprint — versioned paths, authentication for the Agent Assist provider, per-client rate limits and usage metrics, structured error responses, and a citation payload with source URL, title, section, and last modified date. - Enforce entitlement at query time by filtering on permission metadata the ingestion pipeline captures — no restricted passage is ever returned to an unentitled caller. - Provide the Bedrock retrieve-and-generate reference endpoint used for pilot validation in weeks 7–9. - Build the quality dashboard — relevance, grounding, and citation scores trended per run; permission test results; freshness lag per source; API latency and error rate; OpenSearch capacity. - Support the contact center pilot in weeks 7–9 — capture retrieval misses, tune, and re-run the suite before each change ships. - Write the retrieval and API runbooks including model change, re-embedding, and index rebuild procedures with cost implications. - Pair with client platform owners in weeks 9–10 and hand over on the transition readiness checklist. REQUIRED SKILLS & EXPERIENCE - 4+ years as a software or ML engineer, with at least one RAG or enterprise search system delivered to production and measured with an evaluation suite — not just demos. - Hands-on with Amazon Bedrock — Knowledge Bases, Retrieve and RetrieveAndGenerate APIs, embedding models — and Amazon OpenSearch Serverless vector search including hybrid queries and metadata filters. - Python, with experience shipping Lambda-based APIs behind API Gateway including auth, versioning, and throttling. - Infrastructure-as-code with Terraform or AWS CDK and CI for deployment into a client-owned account. - Practical understanding of chunking, embedding model selection, reranking, and the trade-offs between relevance, latency, and cost. - Experience building operational dashboards in CloudWatch or an equivalent tool. PREFERRED QUALIFICATIONS - AWS Certified Generative AI Developer Professional or AWS Certified Machine Learning Engineer Associate. - Experience with RAG evaluation frameworks and metrics — faithfulness, context precision and recall, citation accuracy. - Experience integrating with a contact center Agent Assist product as an API provider. - Familiarity with Bedrock Guardrails and permission-aware retrieval patterns over Microsoft 365 content. When you join Solvd, you'll… - Shape real-world AI-driven projects across key industries, working with clients from startup innovation to enterprise transformation. - Be part of a global team with equal opportunities for collaboration across continents and cultures. - Thrive in an inclusive environment that prioritizes continuous learning, innovation, and ethical AI standards. Ready to make an impact? If you're excited to build things that matter, champion responsible AI, and grow with some of the industry’s sharpest minds. Apply today and let’s innovate together. Solvd is an equal opportunity employer.

Развивать HTTP API и бизнес-логику двух асинхронных Python-сервисов на FastAPI. Проектировать и реализовывать RAG-пайплайны: retrieval, query rewriting, multi-hop поиск, сбор контекста, structured output, цитирование источников и обработку частичных ответов.

— Разработка инструментов для AI-агентов и интеграция с корпоративными системами (1С, почта, файловые ресурсы);

-Разработка инструментов для агентов и интеграций с корпоративными системами (1С, почта, файловые ресурсы). -Обработка документов: договоры, счета, таблицы.

-Развивать HTTP API и бизнес-логику двух асинхронных Python-сервисов на FastAPI. -Проектировать и реализовывать RAG-пайплайны: retrieval, query rewriting, multi-hop поиск, сбор контекста, structured output, цитирование источников и обработку частичных ответов.

Location: Croatia; Georgia; Poland; Romania; Turkey; Czechia Gramian Consultancy is a boutique consultancy specializing in IT professional services and engineering talent solutions. With a strong background in software engineering and leadership, we help companies build high-performing teams by matching them with professionals who truly fit their needs. Role Overview We are looking for an experienced Forward Deployed AI Engineer to design and deliver production-grade AI applications directly with enterprise product and engineering teams. This is a highly hands-on, client-facing role focused on turning complex business requirements into scalable solutions using Python, Large Language Models, RAG, agentic workflows, LangChain / LangGraph, and modern web technologies . You will own the full delivery lifecycle, from discovery and rapid prototyping through production deployment, adoption, and continuous optimization. CONTRACT: Contractor, long-term engagement COMMITMENT: Full-time LOCATIONS: Remote — Turkey, Croatia, Czech Republic, Estonia, Georgia, Poland, or Romania START: ASAP DURATION: 6+ months, with possible extension based on performance NOTES: Fluent English and strong stakeholder communication skills are required. Key Responsibilities Work directly with business, product, and engineering stakeholders to understand complex requirements and translate them into production-ready AI solutions . Design and build applications using LLMs, RAG, AI agents, tool/function calling, and structured outputs . Develop agentic workflows using frameworks such as LangChain, LangGraph, LlamaIndex, or similar technologies . Design retrieval architectures using vector search, embeddings, document pipelines, and contextual retrieval techniques. Build robust backend services and application logic in Python . Develop user-facing AI applications using React, Next.js, or similar frontend technologies . Integrate AI applications with enterprise APIs, internal platforms, data systems, and authentication / IAM environments. Deploy applications using cloud platforms, Docker, and CI/CD pipelines . Monitor and improve AI application quality, reliability, latency, security, and business performance. Contribute reusable engineering patterns, components, and implementation best practices across projects. Requirements 6+ years of professional software engineering experience , with strong production-level Python expertise. Hands-on experience building LLM-powered applications in production. Strong experience with LangChain, LangGraph, LlamaIndex, or comparable AI orchestration frameworks . Strong understanding of Retrieval-Augmented Generation (RAG), vector retrieval, embeddings, and LLM integration patterns . Experience designing AI agents, agentic workflows, tool/function calling, structured outputs, and automation systems . Experience with React, Next.js, or similar modern web technologies . Experience deploying production applications using cloud platforms, Docker, and CI/CD . Strong understanding of enterprise integrations, secure API design, IAM, and AI / LLM-specific security considerations. Strong ability to translate business needs into technical architecture and communicate effectively with both technical and non-technical stakeholders.

Location: Москва Workplace: гибкий Наша команда отвечает за базу знаний и агентов — это ядро платформы, с которым напрямую работают клиенты. Мы строим RAG-системы для корпоративных данных: от загрузки и индексации документов до поиска и генерации ответа. Мы также разрабатываем самих агентов, которые используют эти знания в рабочих сценариях. Задачи Руководить командой разработки направления RAG: отвечать за delivery, качество и стабильность сервисов команды Совмещать управление командой с разработкой — ориентировочно 50% времени работать hands-on Планировать работу команды, декомпозировать и оценивать задачи, управлять выполнением roadmap, багами и техническим долгом Осуществлять техническое лидерство команды: проводить технические проработки, определять архитектуру и технические решения внутри сервисов RAG, проводить code review Участвовать в проработке архитектурных решений на уровне платформы совместно с главным архитектором Разрабатывать и развивать сервисы на Python в области RAG и баз знаний Провести декомпозицию существующих компонентов RAG, реализованных на Ray.io, на отдельные сервисы и участвовать в переходе от текущей архитектуры на Ray к целевой сервисной архитектуре Развивать контур работы с данными и знаниями: загрузку и индексацию данных, парсинг и разбиение документов, embeddings, retrieval, reranking, vector DB, базы знаний и evaluation Отвечать за инженерное качество сервисов команды: тестирование, наблюдаемость, производительность, отказоустойчивость и стабильность в production Участвовать в L4-поддержке сервисов команды и инцидент-менеджменте Проводить people management команды: 1:1, performance review, развитие сотрудников, наём, проведение испытательных сроков Синхронизироваться с продуктовыми и техническими командами платформы по зависимостям и совместным задачам Требования Сильный опыт backend-разработки на Python и опыт самостоятельной разработки production-сервисов Опыт управления командой разработки и ответственность за её delivery Опыт технического лидерства: проектирование сервисов, проведение технических проработок, принятие архитектурных решений, code review Готовность совмещать роль Team Lead с активной разработкой руками Уверенное знание Python, asyncio и FastAPI Опыт работы с PostgreSQL и проектирования схем хранения данных Понимание принципов построения распределённых backend-систем, асинхронной обработки и взаимодействия сервисов Опыт проектирования и декомпозиции сервисной архитектуры, выделения отдельных сервисов из существующих крупных компонентов Опыт работы с брокерами сообщений, желательно NATS Опыт эксплуатации и развития production-сервисов, понимание требований к качеству, стабильности, наблюдаемости и производительности Умение организовывать работу команды: планирование, декомпозиция, оценка задач, управление техническим долгом и багами Опыт people management: развитие сотрудников, обратная связь, performance review и наём Будет плюсом Практический опыт разработки RAG-систем и сервисов баз знаний Опыт работы с retrieval, reranking, embeddings, индексацией, chunking и оценкой качества RAG Опыт работы с Ray/Ray.io и системами, построенными на нём Опыт работы с Qdrant или другими векторными базами данных Опыт работы с Elasticsearch/OpenSearch Опыт работы с Kubernetes Знание Go, опыт разработки сервисов на Go или желание его получить в будущем

Руководить командой разработки направления RAG: отвечать за delivery, качество и стабильность сервисов команды. Совмещать управление командой с разработкой — ориентировочно 50% времени работать hands-on.

Location: Monterrey, Mexico; Montevideo, Uruguay; Remote.AR, Remote.LATAM-country; Remote.BR, Brazil; Remote.CO, Colombia; Rosario, Remote.LATAM-country We are looking for a Senior AI Lead who will act as the primary AI consultant within the organization and help shape the vision, roadmap, and execution of AI initiatives. You will work across stakeholders and delivery teams to guide responsible AI adoption and establish a sustainable AI practice. The AI Practice is focused on building a sustainable AI capability within an enterprise environment and connecting AI initiatives to measurable business impact. It covers strategy, roadmap planning, solution delivery, governance, and adoption enablement across business units. Serve as the primary AI consultant within the organization to identify opportunities for AI driven innovation Develop and drive the AI strategy aligned with business goals Lead the assessment prioritization and roadmap planning of AI initiatives across business units Collaborate with product owners data scientists engineers and business stakeholders to scope design and deliver AI solutions Provide guidance on AI technologies frameworks and architectures including LLMs RAG AI agents prompt engineering and MLOps pipelines Ensure AI projects align with business objectives maintain technical quality and follow ethical AI principles Mentor AI team members to build internal capabilities and support knowledge sharing Track AI trends tools and emerging technologies to support continuous improvement and innovation Manage stakeholder expectations and communicate roadmap progress benefits risks and delivery outcomes Champion AI adoption through workshops demos and enablement sessions Define and track KPIs adoption metrics and ROI frameworks for AI initiatives Establish AI governance frameworks usage policies and guardrails for responsible AI adoption Guide the evolution toward an Agentic SDLC model across software development workflows Drive change management activities to support AI augmented and agent orchestrated ways of working 7+ years of experience in AI consulting solution architecture or related roles with leadership responsibilities Experience with AI and ML technologies including LLMs NLP computer vision conversational AI RAG architectures and MLOps Experience driving AI initiatives from ideation through design and implementation in enterprise environments Experience working with clients and executive leadership to translate business needs into AI solutions Hands on collaboration experience with development teams and data scientists across the AI and ML toolchain including Python PyTorch TensorFlow Jupyter Git and CI/CD pipelines Understanding of AI ethics data privacy and responsible AI principles Experience designing and delivering AI training enablement programs or internal advocacy initiatives that drive measurable adoption Familiarity with AI governance frameworks and organizational policy development for responsible AI use Understanding of Agentic SDLC methodologies and AI native delivery practices Advanced degree in Computer Science AI Data Science or a related field or equivalent professional experience Experience with cloud AI platforms such as AWS SageMaker Bedrock and Amazon Q Developer Experience with cloud AI platforms such as Azure OpenAI Service Azure AI Studio and Cognitive Services Experience with cloud AI platforms such as Google Cloud Vertex AI Gemini API and Document AI Experience with AI lifecycle management and MLOps platforms such as MLflow Kubeflow Weights & Biases and SageMaker Pipelines Experience with AI orchestration and agent frameworks such as LangChain LangGraph CrewAI AutoGen Semantic Kernel and Haystack Familiarity with vector databases and retrieval infrastructure such as Pinecone Weaviate Chroma Qdrant pgvector and Elasticsearch with kNN

📌 Разработка инструментов для агентов и интеграций с корпоративными системами (1С, почта, файловые ресурсы). 📌 Обработка документов: договоры, счета, таблицы.

Design and build production RAG systems and AI agents. Run open-source LLMs self-hosted, from model selection to serving and optimization.

Требования: — Глубокое понимание и опыт работы с Docker и Kubernetes (K8s) — Опыт построения CI/CD for ML — Владение инструментами MLOps: MLflow, ClearML, Kubeflow, DVC — Опыт работы с фреймворками LangChain, LangGraph, LlamaIndex О компании: Проект: построение и развитие MLOps-платформ для машинного обучения с фокусом на LLM-решения, RAG-пайплайны и мультиагентные системы. Важно: Локация/Гражданство: РФ.

Задачи: — Проектировать и развивать внутреннее AI-ядро: агенты, skills, tool use, routing, orchestration. — Строить и улучшать RAG-сценарии: retrieval, chunking, embeddings, reranking, доступ к знаниям. — Делать reusable-компоненты и шаблоны для аналитиков: skills, коннекторы, пайплайны, reference flows. — Интегрировать AI-платформу с внутренними системами, базами данных, API и корпоративными источниками знаний. — Настраивать observability, tracing, evaluation и контроль качества AI-пайплайнов. — Оптимизировать качество, latency, стоимость и устойчивость пайплайнов. Требования: — Практический опыт построения production RAG / agent / workflow-систем. — Опыт создания внутренних инструментов или платформ, которыми реально пользовались аналитики или бизнес-команды. — Умение работать на стыке engineering и enablement: собрать решение и сделать его понятным для пользователей. — Опыт интеграции AI-пайплайнов с корпоративными источниками данных, API и внутренними системами. — Умение разбирать сложные пользовательские сценарии и упаковывать их в reusable-механики платформы. Технический стек: • Python, SQL, FastAPI, API integrations, async, service-oriented development. • LangGraph, LlamaIndex, Haystack, Semantic Kernel. • Qdrant, pgvector, Weaviate, Milvus, OpenSearch, Elasticsearch. • Embeddings, hybrid search, metadata filtering, reranking. • Airflow, Prefect, Dagster, Temporal. • Langfuse, Phoenix, OpenTelemetry (или собственные подходы к tracing / observability / eval). • Docker, Linux, Kubernetes, CI/CD. Условия: — Ставка: обсуждается отдельно. — Готовность к редким командировкам в Ереван. — Работа в топовом финтех-направлении над уникальным AI-продуктом.

Location: Remote This role may require in-person interview/s. About Osano: Osano is an innovative B-Corporation built around a simple belief: privacy is a fundamental right. Since day one, we’ve been focused on giving modern enterprises the ability to innovate quickly, stay competitive, and earn customer trust by respecting data privacy and complying with consent guidelines. Our products help organizations operate confidently in a rapidly evolving regulatory and technological landscape. About the Role: We’re hiring a Senior AI Engineer to build and improve Osano AI, our intelligent privacy assistant. As part of the Osano AI team, you will take capabilities from first idea to production, with an emphasis on delivering capabilities that are reliable, measurable, and trustworthy. This role goes beyond wrapping APIs or building chat interfaces. We are looking for an engineer who has shipped LLM-powered systems that real users depend on. That means that you’ve dealt with hallucinations, latency, cost, evaluation, and tricky edge cases within agentic systems, and you know what it takes to keep these non-deterministic systems working after launch. You will often work on problems that aren’t well-defined. Success here depends on how you think, communicate, and work with others as much as on what you build. This position is ideal for someone who is highly technical, thrives in ambiguity, takes ownership of 0→1  initiatives, wants to have an outsized impact, and believes responsible AI is non-negotiable. Responsibilities: Build and ship production AI Design, build, and maintain agentic features across our products, from early prototype to scaled production deployments. Build retrieval (RAG) systems that build trust through verifiable sources. Build agentic and multi-step workflows with guardrails, observability, and structured outputs. Balance quality, latency, reliability, and cost in production. Measure and improve AI systems Build thorough evaluation frameworks that cover accuracy, grounding, latency, and reliability, utilizing both offline evaluations and real-world production signals. Deploy data-driven methods to decide what to ship, how to fix it, and what to stop doing. Continuously refine deployed systems based on measurable outcomes. Collaborate and communicate Work closely with product managers, engineers, privacy experts, and other stakeholders to decide what to build, how to build it, and why. Write clear design documents and run effective technical discussions. Privacy-First Development Embed privacy-by-design principles across the AI stack. Ensure compliance, data sovereignty, and secure handling practices. Collaborate cross-functionally to align AI capabilities with regulatory standards. About You: You’re curious, self-directed, and motivated to learn quickly. You’re comfortable navigating ambiguity and creating clarity for others. You’re excited about privacy, responsible AI, and building trustworthy technology. You are willing to relentlessly pursue the best, most responsible ways to leverage AI—without overselling its capabilities or ignoring its limitations. Must-Haves: 5+ years of professional software engineering or AI/ML experience Demonstrated experience building and running agentic systems in production with real users, not just prototypes or internal demos. Deep technical understanding of LLM usage and orchestration beyond prompt engineering. Proven experience building complex RAG systems and autonomous AI agents. Clear written and verbal communication. You can explain technical trade-offs to both engineers and non-technical stakeholders. Concrete examples of getting from ambiguity to results. Nice-to-Haves: Strong expertise in the Python AI ecosystem, including FastAPI and LangGraph. Experience with Knowledge Graphs to enhance RAG precision Familiarity with Privacy-Enhancing Technologies (PETs) Experience with data anonymization techniques Background in compliance-heavy or regulated environments Why Us: We're a welcoming team that's dependable, curious, moves fast, and full of talented people We're a B-corporation with a mission that matters, in a high-growth space with significant potential You'll have access to our training program, well-defined career paths, and a leadership team that values professional development Benefits: Competitive-pay compensation and ownership interest/equity Unlimited paid time off, plus a requirement to take at least two weeks off per year We're a Best Place for Working Parents and offer paid parental leave for all new parents Osano sponsors individual premiums on base plans at 100% and dependent premiums at 50% for Medical, Dental, and Vision insurance via Aetna A fully and permanently remote company, so you can work from anywhere in the U.S. Receive a MacBook and $600 to craft your home workspace Annual company trip designed to foster connection, creativity, and fun Mental health benefits with free memberships to mindfulness and talk therapy services A Bit More About Osano Osano is a leading data privacy platform that helps organizations quickly and confidently comply with laws like GDPR, CCPA, and other global privacy regulations. From consent management to vendor risk monitoring, Osano makes complex privacy tasks refreshingly simple, so companies can focus on what they do best, while we handle the rest. We’re backed by top-tier investors including Baird Capital, Jump Capital, and LiveOak, and we’re scaling fast with a multi-year runway and ambitious growth plans. We’ve also been recognized as a Great Place to Work for four years running, with 97% of employees saying Osano is a great place to work. As we grow, we're looking for individuals who lean into modern technologies and smarter systems to drive efficiency, clarity, and speed; both in our product and in how we operate. If you're excited by the idea of helping build a high-growth, privacy-first company that embraces thoughtful automation, emerging tech, and intentional collaboration, we’d love to hear from you. Osano is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, marital status, genetic information, disability, veteran status, or any other characteristic protected by applicable law.

Location: Remote - Colombia Workplace: Remote Qdrant is an open-source vector search engine powering the next generation of AI applications, from semantic search and retrieval-augmented generation (RAG) to AI agents and real-time recommendations. Trusted by global leaders like Canva, HubSpot, Tripadvisor, Bosch, and Deutsche Telekom, we’re building the retrieval infrastructure layer for modern AI. Recently raising $50M in Series B funding, we are growing rapidly and committed to transforming how AI understands and interacts with data. As a remote-first company, we believe diverse backgrounds, perspectives, and experiences fuel innovation. Here, you’ll own meaningful work, tackle challenges, and grow alongside passionate individuals dedicated to shaping the future of AI. THE TEAM AND YOUR IMPACT As a Senior Software Engineer on the Qdrant Cloud Operations team, you will run and evolve the infrastructure behind Qdrant Cloud: the regional Kubernetes clusters, cloud accounts and networking that customer databases run on across AWS, GCP and Azure. You will be one of the engineers in our LATAM on-call group. Together with the team in Europe, you will help us set up a two-region follow-the-sun on-call rota, so incidents in the Americas time zones get a fast response and fewer out-of-hours pages land on the European team. This role includes a regular on-call rotation. Shifts include time outside normal working hours in Americas time zones, and on-call is compensated. WHAT YOU WILL OWN - Operate and improve the regional Kubernetes clusters and cloud infrastructure that host customer Qdrant clusters (AWS, GCP, Azure). - Take part in a follow-the-sun on-call rota covering the Americas time zones: respond to incidents, lead root cause analysis and drive the follow-up fixes. - Turn recurring manual operations into automation and self-service, in Go and infrastructure-as-code (Terraform, Crossplane, GitOps with FluxCD). - Roll out new regions and dedicated regions (Qdrant Cloud regions reserved for a single customer), and make that process faster and more repeatable. - Improve monitoring, alerting and runbooks so problems are caught before customers notice. - Improve the reliability, performance and cost of the fleet: instance types, storage and capacity. - Write design docs for infrastructure changes and lead them through review and rollout, including across teams. - Raise the bar on how the team runs incidents, postmortems and day-to-day operations, and help onboard the next engineers in LATAM. WHO YOU ARE - 5+ years in SRE, infrastructure, platform engineering or DevOps roles, running production systems. - Solid experience running Kubernetes in production, including stateful workloads or databases, and debugging it when it breaks. - Hands-on experience with at least one major cloud provider (AWS, GCP or Azure), including its networking: VPCs, load balancing, DNS, PrivateLink / Private Service Connect. Two or more providers is a plus. - You have built and maintained production software in Go, or in another language and are happy to switch to Go. - Experience with infrastructure-as-code (Terraform/Crossplane or similar), CI/CD and GitOps. - On-call experience: you have handled real production incidents using metrics, logs and alerting (Prometheus, Grafana, OpenTelemetry or similar), and written up what happened. - Clear written and spoken English. Most of the team is in Europe, so a lot of handover is async, and you may occasionally join customer calls to debug escalations that Customer Support can't resolve alone. - You use AI coding and operations tools in your daily work, and you know when to trust their output and when to verify it. NICE TO HAVE - Experience building or debugging Kubernetes operators or controllers. - Experience running GPU workloads on Kubernetes. WHY JOIN US - A remote-first, international team working on cutting-edge AI infrastructure. - A competitive salary with additional perks. - Flexible working hours and async-friendly culture. - High ownership and real impact. - Open-source, engineering-driven culture. - Choose your own laptop equipment. For US-based full-time employees, we also offer a comprehensive benefits package including 401k match, health, dental, and vision insurance, plus flexible PTO policy. Qdrant is an equal-opportunity employer. We believe the best ideas come from diverse teams, and we actively welcome applicants from all backgrounds. If this role excites you but you don't check every single box, we'd still love to hear from you! We don't want to miss out on great people because of a checklist. Come build with us! FOR INFORMATION ON HOW WE HANDLE YOUR PERSONAL DATA, PLEASE REFER TO OUR RECRUITMENT PRIVACY POLICY https://qdrant.tech/legal/recruitment-privacy-policy/ #LI-REMOTE

Location: Remote - EMEA; Spain; Germany Workplace: Remote Qdrant is an open-source vector search engine powering the next generation of AI applications, from semantic search and retrieval-augmented generation (RAG) to AI agents and real-time recommendations. Trusted by global leaders like Canva, HubSpot, Tripadvisor, Bosch, and Deutsche Telekom, we’re building the retrieval infrastructure layer for modern AI. Recently raising $50M in Series B funding, we are growing rapidly and committed to transforming how AI understands and interacts with data. As a remote-first company, we believe diverse backgrounds, perspectives, and experiences fuel innovation. Here, you’ll own meaningful work, tackle challenges, and grow alongside passionate individuals dedicated to shaping the future of AI. THE TEAM AND YOUR IMPACT As a Senior Software Engineer on the Qdrant Cloud Platform team, you will build the shared foundations that every other Cloud team builds on: core control-plane services, authentication, security, developer tooling and the paths into Qdrant Cloud for both people and AI agents. Qdrant Cloud is growing fast. More customers, more regions and more teams shipping features puts more weight on the platform underneath. The role is a mix of running and hardening existing services and building new ones. This role is for engineers who like building for other engineers, care about security and good defaults, and want to see their work speed up a whole engineering org. WHAT YOU WILL OWN - Design, build and operate core control-plane services in Go. - Work on platform-wide concerns other teams depend on: security, audit, and the authentication and authorization plumbing. - Build how people and AI agents sign in to and access Qdrant Cloud (API, CLI, MCP), including OAuth flows. - Improve developer experience and delivery: CI/CD, testing, and safety checks on pull requests, including for AI-written code. - Strengthen observability of the platform through monitoring, logging, alerting and metrics. - Reduce operational load through engineering, so the team spends less time firefighting and more time building. - Take part in on-call for platform services, including incident response and root cause analysis. - Work closely with the Cluster Lifecycle, Operations, and Billing & IAM teams, who depend on the platform every day. WHO YOU ARE - 5+ years of software engineering experience, with a good part of it on platform, backend or infrastructure. - Strong proficiency in Go. - Experience designing and running APIs and backend services in production (gRPC or REST). - Solid experience running services on Kubernetes in production. - Good working knowledge of authentication and authorization (OAuth2/OIDC, tokens, API keys) and secure-by-default design. - Hands-on experience with at least one major cloud provider (AWS, GCP or Azure). - Experience with CI/CD and automated testing. - Comfortable participating in on-call rotations and handling production incidents. - Ownership mindset and clear communication skills. NICE TO HAVE - Experience building internal platforms or developer tooling used by other engineering teams. - Experience with infrastructure-as-code (Terraform or similar). - Background in SaaS, database or systems-level product companies. - Experience with Prometheus, Grafana, OpenTelemetry or similar tooling. WHY JOIN US - A remote-first, international team working on cutting-edge AI infrastructure. - A competitive salary with additional perks. - Flexible working hours and async-friendly culture. - High ownership and real impact. - Open-source, engineering-driven culture. - Choose your own laptop equipment. For US-based full-time employees, we also offer a comprehensive benefits package including 401k match, health, dental, and vision insurance, plus flexible PTO policy. Qdrant is an equal-opportunity employer. We believe the best ideas come from diverse teams, and we actively welcome applicants from all backgrounds. If this role excites you but you don't check every single box, we'd still love to hear from you! We don't want to miss out on great people because of a checklist. Come build with us! FOR INFORMATION ON HOW WE HANDLE YOUR PERSONAL DATA, PLEASE REFER TO OUR RECRUITMENT PRIVACY POLICY https://qdrant.tech/legal/recruitment-privacy-policy/ #LI-REMOTE

Location: Remote - EMEA; Spain; Germany Workplace: Remote Qdrant is an open-source vector search engine powering the next generation of AI applications, from semantic search and retrieval-augmented generation (RAG) to AI agents and real-time recommendations. Trusted by global leaders like Canva, HubSpot, Tripadvisor, Bosch, and Deutsche Telekom, we’re building the retrieval infrastructure layer for modern AI. Recently raising $50M in Series B funding, we are growing rapidly and committed to transforming how AI understands and interacts with data. As a remote-first company, we believe diverse backgrounds, perspectives, and experiences fuel innovation. Here, you’ll own meaningful work, tackle challenges, and grow alongside passionate individuals dedicated to shaping the future of AI. THE TEAM AND YOUR IMPACT Qdrant Cloud is growing fast, and most of the new product features on our 2027 roadmap land with this team. That means a lot of new work: cross-region replication, disaster recovery and new ways to manage clusters. This role is for engineers who like hard distributed systems problems, write Go every day, and want their code to manage real customer databases in production. WHAT YOU WILL OWN - Design and build features in our Kubernetes operator and control-plane services, in Go. - Own the full cluster lifecycle: provisioning, scaling, upgrades, configuration changes, backups and restores. - Design APIs and integrations that let people, and more and more AI agents, manage Qdrant Cloud. - Close the feature gaps between Hybrid Cloud and our managed cloud, and extend Private Cloud. - Make cluster operations safe by default: controlled rollouts, minimal disruption to customers and clear failure handling. - Take part in on-call for the services you build, including incident response and root cause analysis. - Work closely with other teams (Operations, Platform, and Billing & IAM teams, Product and Support). WHO YOU ARE - 5+ years of software engineering experience, with a good part of it on infrastructure, platform or distributed systems. - Strong proficiency in Go. - Experience building Kubernetes operators, controllers or similar orchestration services (reconciliation loops, retries, recovering from partial failures). - Strong understanding of distributed systems: consistency, failure handling, replication. - Hands-on experience with at least one major cloud provider (AWS, GCP or Azure). - Experience designing APIs (gRPC or REST) and with CI/CD and automated testing. - Comfortable participating in on-call rotations and handling production incidents. - Ownership mindset and clear communication skills. NICE TO HAVE - Experience running stateful workloads or databases on Kubernetes. - Background in database, storage or DBaaS products. - Experience with backup, replication or disaster recovery systems. - Experience with Prometheus, Grafana, OpenTelemetry or similar tooling. - Contributions to open-source projects, especially in the Kubernetes ecosystem. WHY JOIN US - A remote-first, international team working on cutting-edge AI infrastructure. - A competitive salary with additional perks. - Flexible working hours and async-friendly culture. - High ownership and real impact. - Open-source, engineering-driven culture. - Choose your own laptop equipment. For US-based full-time employees, we also offer a comprehensive benefits package including 401k match, health, dental, and vision insurance, plus flexible PTO policy. Qdrant is an equal-opportunity employer. We believe the best ideas come from diverse teams, and we actively welcome applicants from all backgrounds. If this role excites you but you don't check every single box, we'd still love to hear from you! We don't want to miss out on great people because of a checklist. Come build with us! FOR INFORMATION ON HOW WE HANDLE YOUR PERSONAL DATA, PLEASE REFER TO OUR RECRUITMENT PRIVACY POLICY https://qdrant.tech/legal/recruitment-privacy-policy/ #LI-REMOTE