Xenon7

Location: Indianapolis, United States; Ohio City, United States; Chicago, United States Description Location: Indianapolis, IN Metro (Hybrid / 3-Day Onsite) (Open to Regional/EST Candidates with Onsite Travel) Contract Type: Contractor Full-Time / Enterprise Project Engagement (Outsourced via Xenon7) 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. Role purpose Lead the development and application of digital twins to improve clinical trial design and support evidence-based clinical development decisions. The role prioritizes digital-twin, simulation, and AI/ML expertise, supported by clinical-development experience and close partnership with trial-design experts. Position overview You will join a multidisciplinary clinical development environment and partner with clinical scientists, physicians, biostatisticians, pharmacometricians, biomarker experts, data scientists, data engineers, and other stakeholders. You will translate clinical development questions into quantitative strategies and deliver  interpretable analyses that inform protocol design, patient selection, endpoint strategy, dosing, operational planning, and decision-making under uncertainty. The role prioritizes digital twins as the primary capability for simulating patients, disease trajectories, treatment response, and clinical trials. The successful candidate will bring strong digital-twin, simulation, and AI/ML expertise, along with sufficient clinical-development knowledge to work effectively with trial teams. Deeper trial-design expertise can be developed through close collaboration with clinical scientists, biostatisticians, and pharmacometricians. Key responsibilities Digital-twin development and validation ·   Lead the design, development, and evaluation of digital twins of patients, disease trajectories, treatment response, or clinical trials using clinical trial and relevant external data. ·   Combine mechanistic, statistical, causal, and machine learning approaches to create fit-for-purpose digital twins that can be simulated under alternative populations, interventions, dosing strategies, endpoints, or trial designs. ·   Define use cases and contexts of use with clinical and quantitative experts so each digital twin addresses a specific trial-design or development decision. ·   Assess calibration, validation, robustness, transportability, uncertainty, and practical relevance using appropriate internal and external evaluation strategies. ·   Develop reproducible workflows and reusable digital-twin components that can be applied across programs or therapeutic areas. Clinical trial design and quantitative decision support ·   Partner with trial teams to frame development questions and evaluate design options, including target populations, eligibility criteria, enrichment strategies, comparators, endpoints, visit schedules, dosing strategies, and operational tradeoffs. ·   Use digital twins and complementary simulation methods to compare plausible scenarios, characterize uncertainty, and support model-informed decision-making. ·   Work closely with statisticians and pharmacometricians so analyses complement established trial-design methods and align with clinical and regulatory objectives. ·   Communicate assumptions, limitations, and decision-relevant insights clearly to technical and non-technical stakeholders. Data integration and scientific collaboration ·   Analyze and integrate clinical trial data and, where relevant, biomarkers, imaging, digital health data, omics, patient-reported outcomes, and real-world data. ·   Partner with data engineering and platform teams to create analysis-ready datasets, data-quality checks, lineage, and reproducible pipelines. ·   Contribute to scientific documentation, presentations, publications, and responsible adoption of digital-twin methods across clinical development. Model quality and responsible use ·   Define the intended use, decision context, success criteria, and limitations of each model before development. ·   Ensure models are interpretable, auditable, reproducible, and supported by evidence appropriate to their intended use. ·   Apply appropriate governance, privacy, security, and scientific-review requirements for clinical data and analytical outputs. ·   Clearly distinguish exploratory analyses from validated decision-support evidence and avoid overclaiming model capability. Requirements ·   PhD in data science, statistics, biostatistics, pharmacometrics, computer science, biomedical informatics, engineering, mathematics, operations research, or a related quantitative field. ·   Demonstrated experience developing digital twins, virtual patients, disease-progression models, clinical trial simulations, synthetic controls, or closely related computational models. ·   Strong experience applying machine learning, statistical modeling, causal inference, or simulation to biomedical, healthcare, pharmaceutical, or other complex scientific data. ·   Strong programming skills in Python, R, or a comparable analytical language, with experience using version-controlled and reproducible workflows. ·   Sufficient clinical-development knowledge to frame relevant questions and collaborate effectively with clinical and quantitative trial experts. ·   Ability and interest to deepen clinical trial-design knowledge through partnership with clinical scientists, biostatisticians, and pharmacometricians. ·   Strong written and verbal communication skills and demonstrated effectiveness in cross-functional scientific collaboration. Preferred qualifications ·   Strongly preferred: experience supporting clinical trial design, protocol development, clinical development strategy, model-informed drug development, or regulatory-facing quantitative evidence. ·   Familiarity with one or more trial-design topics, such as endpoint strategy, sample size or power assumptions, patient selection or enrichment, longitudinal outcomes, missing data, adaptive designs, or decision criteria. ·   Knowledge of digital-twin architecture and validation, supported by expertise in causal inference, Bayesian modeling, survival analysis, longitudinal modeling, clinical trial simulation, or synthetic controls. ·   Experience with clinical data standards and common clinical-development data sources. ·   Experience with biomarker, imaging, omics, digital health, or real-world data and integration of multiple data modalities. ·   Experience with scalable cloud-based analytical environments. ·   Track record of translating analytical work into decisions, reusable capabilities, publications, presentations, or other measurable scientific impact.

Location: Indianapolis, United States; Ohio City, United States; Chicago, United States About us: Shape the Future with AI, Ignite Your Potential Xenon7 is an inferno where skill, dedication and passion run together. About our client: Global healthcare leader headquartered in Indianapolis, Indiana. The Cardiometabolic Research (CMR) Therapeutic Area of our client, focuses on the discovery of biologic, small molecule and genetic therapeutics for the treatment of cardiometabolic diseases and associated complications. The Cloud and Connectivity organization is seeking an engineer in HPC platforms. This role will focus on enabling analytical, statistical, and scientific computing capabilities within high performance computing infrastructure. We are looking for an HPC Platform Engineer to help accelerate the next era of high performance computing innovation. What You’ll Be Doing You will be a leader the engineering and operations of design, build, and maintain scalable HPC platforms. You will play a crucial role in enabling HPC infrastructure and experiences. Collaborate with researchers and scientists to optimize performance and streamline workflows. Leverage tooling and automation for orchestration, resource scheduling, data access, and reproducibility. Evolve and operate public cloud and on-premises environments with a focus on availability and performance for HPC workloads. Define and monitor infrastructure metrics, resource utilization, among others. How You’ll Succeed Be Bold - You will bring a high learning agility and platform engineering skills to enable the Technology strategy, identifying opportunities to accelerate our HPC journey. Be Fast - You will advance initiatives to enable critical business projects. Be Proactive - You will have opportunities to leverage agile ways of working with a willingness to become an expert in deploying HPC solutions. Be Your Best - You will learn about new technologies in HPC. Requirements What You Should Bring Hands-on experience in HPC platforms, including knowledge of accelerators (e.g., GPU), HPC schedulers (e.g., Altair Grid Engine, Slurm), Kubernetes platforms, and containers technologies (Docker, Apptainer). Demonstrated experience in HPC workloads, infrastructure, and cluster architectures. Expertise with the Linux command line, Linux troubleshooting, and HPC administration. Experience with DevOps tools such as Github, Chef, Ansible, Terraform. Experience with automating infrastructure and applications. Passion for continual learning and staying informed of new technologies, infrastructure trends, and approaches in the HPC field. Strong programming and scripting skills in languages such as Python or Bash. Basic Requirements Bachelor’s degree in computer science, Information Technology, or related technical field. 5+ years’ experience HPC Platform Engineer. Demonstrated experience leading a global large-scale infrastructure project. Benefits Attractive, market-leading salary package. Clear career advancement path with professional development opportunities.

Location: Indianapolis, United States; Chicago, United States; Cincinnati, United States; Columbus, United States; West Lafayette, United States; Bloomington, United States Location: Indianapolis, IN Metro (Hybrid / 3-Day Onsite) (Open to Regional/EST Candidates with Onsite Travel) Contract Type: Contractor Full-Time / Enterprise Project Engagement (Outsourced via Xenon7) 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 Senior Machine Learning Engineer with extensive experience in production MLOps, model deployment, and system scaling to drive ML engineering initiatives for a top-tier life sciences client. This role sits at the critical intersection of production ML infrastructure, life science research, and manufacturing process engineering. In this position, you will own the architectural design and hands-on execution of production ML systems, model integration APIs, and scalable MLOps pipelines. You will bridge complex domains—from computational biology, small and large molecule research, and clinical trial analytics to active pharmaceutical ingredient (API) manufacturing processes, batch optimization, and industrial automation ML. Operating in a 3-day onsite hybrid capacity in Indianapolis, you will collaborate directly with process engineers, life science researchers, and platform engineering teams to build robust, low-latency ML systems that scale across the enterprise. Key Responsibilities Production MLOps & Systems Architecture Design, deploy, and maintain robust, production-grade MLOps pipelines and infrastructure for continuous model training, deployment, versioning, and monitoring. Implement automated model drift detection, performance monitoring, and self-healing inference pipelines in high-reliability environments. Process Engineering & Manufacturing ML Integration Operationalize and integrate production ML models into operational technology (OT), API manufacturing workflows, and chemical process control systems. Deploy predictive models for batch processing, process control optimization, real-time quality assurance, and facility automation use cases. Scalable Inference & System Integration Build low-latency, high-throughput microservices and serving architectures (FastAPI, Triton Inference Server, TorchServe) for model deployment into live production applications. Containerize and orchestrate ML workloads across distributed cloud and edge systems using Kubernetes, Docker, and modern pipeline engines (Kubeflow, MLflow). Technical Leadership & Domain Alignment Partner directly with chemical engineers, computational biologists, and software architects to translate operational friction into production-ready ML engineering solutions. Establish enterprise MLOps standards, model governance, and CI/CD best practices across the full machine learning operational lifecycle. Requirements Experience & Mindset Experience: Senior-level proficiency (10–20+ years) in software engineering, MLOps, production ML system deployment, and infrastructure scaling. Domain Adaptability: Demonstrated ability to deploy and maintain production ML systems across non-standard, highly specialized domains (e.g., transition between process/chemical engineering ML and clinical/scientific research applications). Location & Work Auth: Must hold unrestricted US Work Authorization (no sponsorship available) and be able to work 3 days per week onsite in the Indianapolis, IN area. Culture & Communication: Pragmatic problem-solving mindset, strong collaborative drive, and the ability to articulate complex MLOps architecture to cross-functional engineering teams. Must-Have Technical Stack Languages & Frameworks: Advanced Python, C++, and deep proficiency with PyTorch, TensorFlow, or Scikit-learn. MLOps & Serving: Proven expertise with Triton Inference Server, TorchServe, MLflow, Kubeflow, or Databricks ML runtime. Infrastructure & Orchestration: Hands-on expertise with Kubernetes, Docker, CI/CD pipelines, FastAPI/gRPC, and cloud platform ecosystems (AWS/Azure). Monitoring & Integration: Experience building real-time model monitoring, feature stores, drift detection systems, and integration with enterprise data pipelines. Domain Competency (Scientific & Process Focus) Deep exposure to applying ML models in either scientific/clinical domains (drug discovery, small/large molecule, computational biology) OR chemical/process engineering environments (API manufacturing, batch processing, SCADA/MES integration, process optimization). Nice-to-Haves & Certifications Academic background in Chemical Engineering, Bio-process Engineering, Computer Science, or a related STEM discipline. Direct experience operationalizing ML models inside regulated GxP environments in the Life Sciences or Specialty Chemicals sectors. Certifications: AWS Certified Machine Learning – Specialty, Databricks Certified Machine Learning Professional, or equivalent MLOps credentials. What This Role Is NOT Not a Data Scientist or Exploratory R&D Specialist: You will not be focusing on exploratory data analysis, academic algorithms, or standalone Jupyter notebook modeling; you are building, scaling, and maintaining production MLOps pipelines, inference engines, and model integration code. Not a non-coding Architect: This is a 100% hands-on MLOps and software engineering lead role requiring direct model deployment, infrastructure creation, and technical execution. Not a Fully Remote Position: This role requires a steady hybrid commitment of 3 days onsite per week at the client site in Indianapolis.

Location: Indianapolis, United States; Chicago, United States; Cincinnati, United States; Columbus, United States; West Lafayette, United States; Bloomington, United States Location: Indianapolis, IN Metro (Hybrid / 3-Day Onsite) (Open to Regional/EST Candidates with Onsite Travel) Contract Type: Contractor Full-Time / Enterprise Project Engagement (Outsourced via Xenon7) 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 Senior Data Engineer with extensive experience in data architecture and platform engineering to drive data initiatives for a top-tier life sciences client. This role sits at the critical intersection of scientific research informatics, clinical data systems, and manufacturing process engineering. In this position, you will own the architectural vision and hands-on execution of scalable data platforms and pipelines. You will bridge complex data domains—from small and large molecule research, genomics, and clinical trial datasets to active pharmaceutical ingredient (API) manufacturing processes, batch data, and industrial control systems. Operating in a 3-day onsite hybrid capacity in Indianapolis, you will collaborate directly with process engineers, clinical scientists, and platform teams to build high-performance data infrastructure that accelerates drug discovery and manufacturing operations. Key Responsibilities Scientific & Clinical Data Platform Architecture Design, build, and maintain production-grade data pipelines and architecture tailored for scientific, clinical trial, and research informatics data (small/large molecule, genomics, proteomics, LIMS). Structure complex, multi-modal clinical and scientific datasets to enable advanced analytics, enterprise reporting, and downstream machine learning models. Process Engineering & Manufacturing Integration Ingest, harmonize, and model operational technology (OT) and manufacturing process datasets, including API manufacturing pipelines, batch processing data, MES, SCADA, and OSIsoft PI systems. Unify disparate laboratory and facility data pipelines into centralized, highly available enterprise data platforms. Enterprise Data Engineering & Compliance Build robust ETL/ELT pipelines using modern cloud platforms (Databricks, Snowflake, AWS/Azure), PySpark, and SQL. Ensure all data pipelines and platform integrations strictly adhere to enterprise governance, data residency, and GxP regulatory standards within a heavily monitored environment. Technical Leadership & Domain Alignment Partner directly with process engineers, chemical engineering leads, and research informatics directors to translate operational friction into robust technical specifications. Establish engineering best practices, data modeling standards, and pipeline monitoring frameworks across the enterprise data stack. Requirements Experience & Mindset Experience: Senior-level proficiency (10–20+ years) in software development, data platform architecture, and complex ETL/ELT engineering. Domain Adaptability: Demonstrated ability to engineer data pipelines across non-standard, highly specialized domains (e.g., transition between process/chemical engineering data and clinical/scientific research informatics). Location & Work Auth: Must hold unrestricted US Work Authorization (no sponsorship available) and be able to work 3 days per week onsite in the Indianapolis, IN area. Culture & Communication: Exceptional problem-solving mindset, strong adaptability, and the ability to articulate complex technical architecture to cross-functional engineering teams. Must-Have Technical Stack Languages & Frameworks: Advanced Python, PySpark, and expert-level SQL. Data Platforms: Hands-on expertise with Databricks, Snowflake, or AWS/Azure enterprise data ecosystems. Orchestration & ETL: Extensive experience with Airflow, dbt, Spark, and enterprise data orchestration engines. Data Pipelines: Proven track record building streaming and batch data architectures via REST APIs, message brokers, and database integrations. Domain Competency (Scientific & Process Focus) Deep exposure to either scientific/clinical informatics (CDISC/SDTM, LIMS, clinical trials, multi-omics) OR chemical/process engineering data (API manufacturing, batch data, SCADA, MES, OSIsoft PI). Nice-to-Haves & Certifications Academic background in Chemical Engineering, Bio-process Engineering, Computer Science, or a related STEM discipline. Direct experience working inside regulated GxP environments in the Life Sciences or Specialty Chemicals sectors. Certifications: Databricks Certified Data Engineer Senior/Professional, Snowflake SnowPro Core/Advanced, or AWS Data Engineer Associate/Professional. What This Role Is NOT Not a pure Data Scientist or ML Researcher: You will not be building or training machine learning models; you are designing and scaling the underlying data architecture, pipelines, and platform infrastructure. Not a non-coding Architect: This is a 100% hands-on engineering lead role requiring direct pipeline construction and technical execution. Not a Fully Remote Position: This role requires a steady hybrid commitment of 3 days onsite per week at the client site in Indianapolis.

Location: Barcelona, Spain; Mataró, Spain; Castelldefels, Spain; Cerdanyola del Vallès, Spain; Sant Cugat del Vallès, Spain; Terrassa, Spain Our Client's Digital Finance IT is building an AI-enablement layer on top of our enterprise data platform to enable business users across Finance to interact with governed data in natural language. We're hiring a Data Enablement Engineer to design, build, and operate the trusted datasets, semantic models, and embedded AI experiences that make this possible. This is a data platform engineering role , not a data science or model-building role. You will spend your time engineering the data foundation that makes AI reliable — semantic layers, governed data products, and embedded natural-language analytics — not training models. What You'll Do Design and build AI-ready data products on Databricks — trusted datasets with well-defined business semantics, KPIs, hierarchies, and business glossary alignment Implement semantic layers and governed datasets that support both traditional BI consumption and natural-language querying by business users Deploy and operate Databricks Genie spaces with Unity Catalog, tuning them for accuracy, adoption, and business relevance Build RAG pipelines and conversational analytics applications grounded in governed enterprise data — including Streamlit or Databricks Apps that let business users query data without writing SQL Engineer robust ETL/ELT pipelines (dbt, Airflow, Snowpark, PySpark) that produce and maintain the trusted data these AI experiences depend on Implement data governance — RBAC, row/column-level security, masking, lineage, auditability, catalog and metadata management — in a regulated pharma environment Optimize cost and performance on both the data platform side (warehouse sizing, cluster tuning, query optimization) and the AI side (token usage, caching, model routing) Partner with Finance business stakeholders to translate domain requirements into semantic models and governed data products they can trust Requirements Must-Have Experience 5+ years hands-on data engineering on cloud data platforms — Databricks demonstrated in real project delivery, not skill-list-only Direct hands-on experience with Databricks Genie — you have built, configured, and tuned these in production or advanced pilots, with specific reference to the flavors used (Cortex Analyst / Search / Agents / LLM Functions, or Genie spaces with semantic models) Semantic layer / trusted data product delivery — you have built governed datasets that business users can rely on, with KPI definitions, hierarchies, and business glossary alignment dbt, PySpark, Snowpark, SQL, Python — strong across the modern data stack Orchestration with Airflow, Databricks Workflows, or equivalent Data governance in regulated environments — RBAC, RLS, masking, lineage, auditability Experience integrating structured and unstructured data (PDFs, SharePoint/Teams content, enterprise knowledge sources) into AI-enablement workflows Nice to Have Pharma, life sciences, or regulated financial services domain experience Veeva CRM, IQVIA, SAP, or clinical data source integration Streamlit or Databricks Apps for business-facing analytics Databricks Data Engineer Professional certification LangChain, LlamaIndex, or equivalent RAG frameworks Cost optimization on both compute (warehouse/cluster) and LLM (tokens/caching/routing) dimensions What We're NOT Looking For Data Scientists — this role is not model training, fine-tuning, LoRA/RLHF, or ML research Pure Data Engineers who list Cortex or Genie as a skill but haven't shipped it in production AI/GenAI engineers whose center of gravity is LangChain agents or RAG-over-documents, without a strong governed data platform foundation Computer vision, NLP model builders, or multi-agent orchestration specialists — wrong shape for this role

Location: Barcelona, Spain; Sant Cugat del Vallès, Spain; Zaragoza, Spain; Girona, Spain; Reus, Spain; València, Spain Location: Barcelona (Hybrid / Flexible) Contract Type: Contractor 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: 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: India; Serbia; Croatia; Spain; Italy Our Client's Digital Finance IT is building an AI-enablement layer on top of our enterprise data platform to enable business users across Finance to interact with governed data in natural language. We're hiring a Data Enablement Engineer to design, build, and operate the trusted datasets, semantic models, and embedded AI experiences that make this possible. This is a data platform engineering role, not a data science or model-building role. You will spend your time engineering the data foundation that makes AI reliable — semantic layers, governed data products, and embedded natural-language analytics — not training models. What You'll Do Design and build AI-ready data products on Databricks — trusted datasets with well-defined business semantics, KPIs, hierarchies, and business glossary alignment Implement semantic layers and governed datasets that support both traditional BI consumption and natural-language querying by business users Deploy and operate Databricks Genie spaces with Unity Catalog, tuning them for accuracy, adoption, and business relevance Build RAG pipelines and conversational analytics applications grounded in governed enterprise data — including Streamlit or Databricks Apps that let business users query data without writing SQL Engineer robust ETL/ELT pipelines (dbt, Airflow, PySpark) that produce and maintain the trusted data these AI experiences depend on Implement data governance — RBAC, row/column-level security, masking, lineage, auditability, catalog and metadata management — in a regulated pharma environment Optimize cost and performance on both the data platform side (warehouse sizing, cluster tuning, query optimization) and the AI side (token usage, caching, model routing) Partner with Finance business stakeholders to translate domain requirements into semantic models and governed data products they can trust Requirements Must-Have Experience 5+ years hands-on data engineering on cloud data platforms — Databricks demonstrated in real project delivery, not skill-list-only Direct hands-on experience with Databricks Genie — you have built, configured, and tuned these in production or advanced pilots, with specific reference to the flavors used (Genie spaces with semantic models) Semantic layer / trusted data product delivery — you have built governed datasets that business users can rely on, with KPI definitions, hierarchies, and business glossary alignment dbt, PySpark, SQL, Python — strong across the modern data stack Orchestration with Airflow, Databricks Workflows, or equivalent Data governance in regulated environments — RBAC, RLS, masking, lineage, auditability Experience integrating structured and unstructured data (PDFs, SharePoint/Teams content, enterprise knowledge sources) into AI-enablement workflows Nice to Have Pharma, life sciences, or regulated financial services domain experience Veeva CRM, IQVIA, SAP, or clinical data source integration Streamlit or Databricks Apps for business-facing analytics Databricks Data Engineer Professional certification LangChain, LlamaIndex, or equivalent RAG frameworks Cost optimization on both compute (warehouse/cluster) and LLM (tokens/caching/routing) dimensions What We're NOT Looking For Data Scientists — this role is not model training, fine-tuning, LoRA/RLHF, or ML research Pure Data Engineers who list Cortex or Genie as a skill but haven't shipped it in production AI/GenAI engineers whose center of gravity is LangChain agents or RAG-over-documents, without a strong governed data platform foundation Computer vision, NLP model builders, or multi-agent orchestration specialists — wrong shape for this role

Location: New Cairo City, Egypt About us: Where elite tech talent meets world-class opportunities! At Xenon7, we work with leading enterprises and innovative startups on exciting, cutting-edge projects that leverage the latest technologies across various domains of IT including Data, Web, Infrastructure, AI, and many others. Our expertise in IT solutions development and on-demand resources allows us to partner with clients on transformative initiatives, driving innovation and business growth. Whether it's empowering global organizations or collaborating with trailblazing startups, we are committed to delivering advanced, impactful solutions that meet today’s most complex challenges. About the Client: Join one of Egypt’s premier financial institutions, renowned for its extensive suite of banking services, including Institutional Banking, Personal Banking, and Islamic Banking. With a global presence through over 50 branches and correspondents, we serve a diverse and dynamic clientele. As we embark on a groundbreaking digital transformation journey, we are committed to leveraging the latest technologies to establish a state-of-the-art data architecture that will redefine our performance and service delivery. Key Responsibilities: Drive "Compliance and Security by Design": Proactively architect and embed compliance guardrails directly into the lifecycle of all Data and AI initiatives, anticipating evolving Central Bank of Egypt (CBE) cyber security regulations and the Egyptian Data Protection Law to eliminate compliance risks before models and data pipelines are deployed. Execute Predictive Threat Modeling for AI: Lead advanced, preventative threat modeling and vulnerability assessments tailored specifically for machine learning models, LLMs, and big data repositories, neutralizing emerging AI-specific threats (such as data poisoning, adversarial attacks, and model inversion) during the early design phases. Establish Continuous Automated Monitoring: Implement and manage automated, real-time vulnerability scanning and continuous compliance monitoring systems across the bank's data infrastructure, ensuring any drift from security standards is detected and remediated before it can be exploited. Foster a Proactive Security Culture with AI Teams: Partner closely with data scientists, AI engineers, and data governance teams to champion secure coding and data handling practices, conducting proactive risk-readiness workshops to prevent human-error vulnerabilities in algorithmic workflows. Develop Next-Gen Incident-Prevention Playbooks: Devise and stress-test predictive incident-response playbooks tailored for AI assets, leveraging global threat intelligence to simulate potential data breaches and algorithmic failures, ensuring the bank’s defenses are常に (always) one step ahead of sophisticated cyber threats. Requirements Professional Experience: 4 + years of progressive experience in cybersecurity compliance, risk management, or vulnerability assessment, with a mandatory and proven track record of working within a regulated banking or financial services institution. Educational Background & Certifications: Bachelor’s degree from a recognized institution in Computer Science, Cybersecurity, Computer Engineering, or a strictly relevant technical field. Possessing elite industry certifications such as CISSP, CISM, CRISC, or CISA is highly preferred. Regulatory & AI Domain Expertise: Comprehensive understanding of the Central Bank of Egypt (CBE) cybersecurity frameworks and regulations, as well as the Egyptian Data Protection Law , paired with foundational knowledge of data science workflows, big data architecture, and emerging AI security frameworks (e.g., OWASP Top 10 for LLMs). Technical Compliance & Threat Modeling Skills: Demonstrated expertise in deploying automated vulnerability scanning tools and leading predictive threat modeling exercises specifically tailored to mitigate risks like data poisoning, adversarial attacks, and unauthorized data exposure within complex data pipelines. Benefits Attractive, market-leading salary package Clear career advancement path with professional development opportunities

Location: Egypt; India; Pakistan; Serbia; Croatia; Poland About Us: Where elite tech talent meets world-class opportunities! At Xenon7, we work with leading enterprises and innovative startups on exciting, cutting-edge projects that leverage the latest technologies across various domains of IT including Data, Web, Infrastructure, AI, and many others. Our expertise in IT solutions development and on-demand resources allows us to partner with clients on transformative initiatives, driving innovation and business growth. Whether it's empowering global organizations or collaborating with trailblazing startups, we are committed to delivering advanced, impactful solutions that meet today’s most complex challenges. About the Client: Join one of Egypt’s premier financial institutions, renowned for its extensive suite of banking services, including Institutional Banking, Personal Banking, and Islamic Banking. With a global presence through over 50 branches and correspondents, we serve a diverse and dynamic clientele. As we embark on a groundbreaking digital transformation journey, we are committed to leveraging the latest technologies to establish a state-of-the-art data architecture that will redefine our performance and service delivery. About the Role We are seeking a seasoned Senior Azure Administrator with a powerful dual background in both Cloud and On-Premises infrastructure to join our elite Data & AI department. In the Egyptian banking sector, compliance and data residency require a sophisticated hybrid approach. In this role, you won't just manage the cloud; you will be the critical bridge connecting our heavy on-premises data legacy systems (like enterprise data warehouses and local servers) with cutting-edge Azure environments. You will ensure seamless data pipelines, high availability, and airtight security across our entire hybrid data estate. Key Responsibilities Infrastructure Integration: Design, deploy, and maintain the connective tissue between our on-premises data centers and Azure. Cloud Infrastructure Management for Data & AI: Provision, configure, and optimize enterprise-grade Azure resources tailored for data workloads, including Microsoft Fabric, Azure Databricks, Azure Data Factory, Synapse Analytics, and Azure Machine Learning environments. On-Prem & Cloud Security Hardening: Enforce strict identity governance across hybrid environments by aligning on-premises Active Directory (AD) with Microsoft Entra ID . Manage secrets across environments and implement network security boundaries that satisfy Central Bank of Egypt (CBE) regulations. Automation & Infrastructure as Code (IaC): Standardize deployment processes across both cloud and local infrastructure using Terraform or Azure Bicep , integrating them into CI/CD pipelines via Azure DevOps to support unified DataOps workflows. Hybrid Disaster Recovery & Monitoring: Architect and test robust Backup and Disaster Recovery (BCDR) strategies that guarantee zero data loss across both local storage arrays and Azure Data Lakes, maintaining continuous visibility through unified monitoring tools. Requirements On-Premises Infrastructure (STRICT MUST): Deep, hands-on experience managing traditional on-premises enterprise data centers is non-negotiable. You must have a strong background in virtualization (VMware/Hyper-V), physical networking, storage area networks (SAN), and Windows/Linux server administration before or alongside your cloud career. Professional Experience: 5 + years of total experience in IT infrastructure/system administration, with at least 3+ years of dedicated, hands-on experience specializing in Azure Cloud Administration within a complex, hybrid architecture. Banking & Financial Sector Experience: Mandatory prior experience working inside a regulated bank or financial institution in Egypt, showing a clear understanding of CBE cloud governance, local data residency laws, and audit readiness. Technical Mastery of Data Ecosystems: Proven familiarity with connecting cloud services to on-premises data repositories (such as SQL Server or Teradata). Solid knowledge of containerized environments ( AKS/Docker ) and enterprise data pipelines is highly advantageous. Education & Elite Certifications: * Bachelor’s degree in Computer Science, Computer Engineering, Information Technology, or a relevant technical field. Microsoft Certified: Azure Administrator Associate (AZ-104) is required. Preferred: Certifications reflecting hybrid/security expertise, such as Azure Stack Hub/Arc familiarity or Azure Security Engineer (AZ-500) . Soft Skills: Exceptional troubleshooting skills for complex hybrid routing or latency issues, a proactive mindset, and fluency in English. Benefits Attractive, market-leading salary package Clear career advancement path with professional development opportunities

Location: Serbia; Croatia; Poland; Belgium; India; Pakistan About Us: Where elite tech talent meets world-class opportunities! At Xenon7, we work with leading enterprises and innovative startups on exciting, cutting-edge projects that leverage the latest technologies across various domains of IT including Data, Web, Infrastructure, AI, and many others. Our expertise in IT solutions development and on-demand resources allows us to partner with clients on transformative initiatives, driving innovation and business growth. Whether it's empowering global organizations or collaborating with trailblazing startups, we are committed to delivering advanced, impactful solutions that meet today’s most complex challenges. About the Client: Join one of Egypt’s premier financial institutions, renowned for its extensive suite of banking services, including Institutional Banking, Personal Banking, and Islamic Banking. With a global presence through over 50 branches and correspondents, we serve a diverse and dynamic clientele. As we embark on a groundbreaking digital transformation journey, we are committed to leveraging the latest technologies to establish a state-of-the-art data architecture that will redefine our performance and service delivery. About the Role As a leading financial institution in Egypt, we are heavily investing in localized AI and Machine Learning capabilities to drive innovation while maintaining total data sovereignty. Our Cybersecurity Department is looking for a highly skilled AI Cybersecurity Engineer with a deeply offensive mindset. Operating entirely within our secure, high-performance on-premises infrastructure , you will act as an internal adversary. Your mission is to proactively threat model, simulate advanced cyberattacks (Red Teaming), and manage algorithmic risks to detect and neutralize vulnerabilities in our AI systems before they can be exploited. Key Responsibilities Predictive AI Threat Modeling: Lead advanced threat-modeling exercises across the bank’s localized AI/ML pipelines, data repositories, and training environments. Anticipate sophisticated attacker behaviors to design preemptive security guardrails before models are moved to production. AI Red Teaming & Offensive Simulations: Conduct targeted offensive operations and ethical hacking against our on-premises AI infrastructure. Simulate real-world adversarial attacks, including data poisoning, model inversion, membership inference, evasion attacks, and prompt injection . Framework Mapping with MITRE ATLAS: Implement and operationalize the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework. Map identified vulnerabilities to ATLAS tactics and techniques to build a robust, AI-specific defensive matrix aligned with traditional MITRE ATT&CK. On-Premises Infrastructure Hardening: Assess and secure the physical and virtual infrastructure hosting our AI workloads, including bare-metal GPU clusters, local containerized environments (Kubernetes/OpenShift), and private data registries, ensuring strict isolation from external threats. Proactive Risk Management & Early Detection: Collaborate with the SOC and data engineering teams to build predictive detection use-cases and custom alerts. Identify the early warning signs of an AI-targeted attack or data exfiltration attempt before malicious actors can compromise model integrity. Requirements Professional Experience: 5+ years of dedicated experience in Offensive Security, Red Teaming, or Advanced Penetration Testing, with a proven pivot and at least 2 years of hands-on experience securing or attacking AI/ML workloads . Banking Sector Experience (Mandatory): Prior experience working within a regulated financial institution in Egypt. You must understand the security constraints, compliance mandates, and audit rigor required by the Central Bank of Egypt (CBE) for on-premises operations. Deep Mastery of AI Attack Frameworks: Expert-level knowledge of MITRE ATLAS and the OWASP Top 10 for Large Language Models (LLMs) . You must have a proven track record of translating these theoretical frameworks into practical, automated offensive playbooks. On-Prem & Core Technical Skills: * Strong proficiency in Python or bash scripting to build custom exploit or testing tools. Deep understanding of on-premises infrastructure components (Linux administration, private networking, storage area networks). Direct experience with container security ( Docker, Kubernetes ) and secure MLOps pipelines. Education & Certifications: * Bachelor’s degree in Computer Science, Cybersecurity, Computer Engineering, or a strictly related field. Highly valued offensive certifications: OSCP, OSCE, CRTP, or GPEN , alongside any specialized AI/ML security credentials. Mindset & Communication: A relentlessly curious, adversarial mindset balanced with strong risk management disciplines. The ability to clearly articulate complex, highly technical AI vulnerabilities into actionable remediation plans for data scientists and senior banking executives. Fluency in English. Benefits Attractive, market-leading salary package Clear career advancement path with professional development opportunities

Location: India; Pakistan; Serbia; Croatia; Poland; Georgia About us: Where elite tech talent meets world-class opportunities! At Xenon7, we work with leading enterprises and innovative startups on exciting, cutting-edge projects that leverage the latest technologies across various domains of IT including Data, Web, Infrastructure, AI, and many others. Our expertise in IT solutions development and on-demand resources allows us to partner with clients on transformative initiatives, driving innovation and business growth. Whether it's empowering global organizations or collaborating with trailblazing startups, we are committed to delivering advanced, impactful solutions that meet today’s most complex challenges. About the Client: Join one of Egypt’s premier financial institutions, renowned for its extensive suite of banking services, including Institutional Banking, Personal Banking, and Islamic Banking. With a global presence through over 50 branches and correspondents, we serve a diverse and dynamic clientele. As we embark on a groundbreaking digital transformation journey, we are committed to leveraging the latest technologies to establish a state-of-the-art data architecture that will redefine our performance and service delivery. About the Role: As a premier banking institution in Egypt, we are rapidly scaling our cutting-edge AI and Machine Learning capabilities. To ensure this innovation does not compromise our security posture, our Cybersecurity Department is looking for an expert AI Cybersecurity Architect . In this highly strategic role, you will be the foundational architect behind the bank’s AI security governance. You will not just react to threats; you will build the comprehensive control frameworks, policies, and architectural standards that govern how AI models, Large Language Models (LLMs), and big data pipelines are safely developed, deployed, and managed across the enterprise. Key Responsibilities Establish AI Security Governance Frameworks: Author, implement, and maintain the bank's enterprise-wide AI Security Policy. Align our internal AI governance with international standards (such as NIST AI RMF, ISO/IEC 42001, and OWASP Top 10 for LLMs) while strictly ensuring compliance with evolving Central Bank of Egypt (CBE) regulations and the Egyptian Data Protection Law. Design & Build Cybersecurity Controls: Architect, enforce, and validate technical and administrative security controls tailored for the entire AI/ML lifecycle (Data Ingestion, Training, Deployment, and Inference). This includes building robust controls against data poisoning, model inversion, prompt injection, and unauthorized data exfiltration. Secure MLOps Architecture: Define the reference architectures and secure guardrails for our Machine Learning Operations (MLOps) pipelines. Ensure that security checks, vulnerability scanning, and model integrity verifications are seamlessly integrated into our continuous deployment workflows. Advanced AI Threat Modeling & Risk Assessment: Lead comprehensive risk assessments and threat-modeling exercises on all proprietary and third-party AI implementations. Identify structural risks in algorithmic logic, training data pipelines, and API integrations, translating technical risks into clear business governance metrics. Third-Party & Vendor AI Governance: Develop and execute rigorous cybersecurity assessment frameworks for evaluating third-party AI tools, cloud-hosted models, and external vendors, ensuring they meet the bank’s strict data privacy and control standards. Requirements Professional Experience: 8+ years of progressive experience in Cybersecurity Architecture or IT Risk Governance, with at least 3+ years of dedicated experience explicitly focused on securing AI/ML systems and building enterprise governance frameworks . Banking Sector Expertise (Mandatory): Minimum of 3 years of experience working within a regulated financial institution in Egypt. You must possess an intricate understanding of CBE cybersecurity circulars, auditing standards, and banking compliance requirements. Mastery of Control Building & Governance: Proven track record of designing, writing, and implementing complex cybersecurity control frameworks from scratch. Deep familiarity with traditional frameworks (NIST SP 800-53, ISO 27001) as well as modern AI-specific guardrails is a must. Technical & Architectural Depth: Strong conceptual and practical understanding of AI/ML infrastructure, including neural networks, LLM orchestration layers, vector databases, and cloud data platforms (Azure, AWS, or hybrid environments). You must speak the language of both data scientists and enterprise security engineers. Education & Elite Certifications: * Bachelor’s degree in Cybersecurity, Computer Engineering, Computer Science, or a related technical discipline. Elite security certifications are highly preferred (e.g., CISSP, CISM, CRISC, or CCSP ), alongside any specialized AI security credentials. Leadership & Communication: Exceptional communication and stakeholder management skills. You must possess the professional maturity to present complex AI risks to C-suite executives and board members, while maintaining the technical credibility to influence data science teams. Fluency in English. Benefits Attractive, market-leading salary package Clear career advancement path with professional development opportunities