Senior Lead Engineer

Capgemini is a global business and technology transformation partner with 340,000 team members in more than 50 countries. It helps organizations accelerate their transition to a digital and sustainable world, delivering end-to-end services and solutions with strengths in AI, cloud, and data. The Group reported 2025 global revenues of EUR 22.5 billion.

• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, AI, or related field.
• Strong hands-on experience in software engineering, ideally in AI/ML, LLM, platform, cloud, or enterprise product environments.
• Extended Python expertise in AI, LLM, and ML solutions including backend service development, data processing, model integration, orchestration, evaluation, and automation.
• Strong frontend development experience with React or comparable modern frontend frameworks.
• Strong backend development experience with JavaScript/TypeScript, Node.js, and frameworks such as Express.js or NestJS.
• Solid understanding of AI/ML concepts, LLM-based application patterns, RAG, embeddings, vector search, prompt engineering, model evaluation, and LLM orchestration.
• Experience with Python frameworks and tools such as FastAPI, Flask, LangChain, LangGraph, Pytest, Pandas, NumPy or similar.
• Experience designing and consuming APIs, building microservices, and integrating services across frontend, backend, and AI layers.
• Experience integrating with cloud platforms, preferably GCP, including Cloud Run, BigQuery, Firestore, Cloud Storage, Vertex AI, or Apigee.
• Solid understanding of authentication, authorization, RBAC, observability, logging, performance, and secure enterprise delivery.
• Ability to work across structured and unstructured data flows including document processing, retrieval pipelines, and enterprise knowledge integration.
• Experience with CI/CD, Git-based workflows, automated testing, code reviews, Docker, and production deployment practices.
• Strong problem-solving skills to debug complex issues across frontend, backend, data, AI model behavior, and platform integration.

Bonus:

• Experience with agentic systems, tool calling, multi-agent orchestration, LLM-as-a-judge patterns, or AI evaluation frameworks.
• Experience with MLOps / LLMOps, model monitoring, prompt evaluation, or AI quality measurement.
• Experience with enterprise AI products, chatbot platforms, conversational interfaces, or knowledge-based assistants.

Technical Delivery:

• Drive the technical delivery of Neo Enterprise capabilities across frontend, backend, AI/LLM services, and integration layers.
• Lead hands-on engineering work and guide developers.
• Translate complex business requirements into scalable, secure, and production-ready solutions.

Frontend Excellence:

• Lead design and implementation of interactive, scalable, user-centric interfaces using React or comparable frameworks.
• Ensure usability, accessibility, and frontend performance.

Backend Engineering:

• Architect, build, and maintain secure, high-performance backend services using JavaScript/TypeScript with Node.js frameworks such as Express.js or NestJS.

AI/LLM Engineering:

• Develop and integrate Python-based AI/LLM capabilities including orchestration flows, RAG pipelines, model interaction logic, evaluation routines, and data-processing components.

Full-Stack AI Product Delivery:

• Connect frontend experiences, backend services, and AI/LLM capabilities into reliable end-to-end product flows across UI, APIs, routing layer, data sources, and cloud services.

Collaboration and Support:

• Collaborate with Architecture, Routing Layer, Apigee, Data, and Backend teams to ensure secure, scalable, and maintainable integration patterns.
• Drive technical problem-solving for complex AI platform topics including latency, grounding quality, data flow, backend troubleshooting, and integration issues.
• Ensure high engineering standards across code quality, testing, observability, performance, maintainability, and production readiness.
• Mentor engineers, review technical designs and code, and support consistent delivery practices across the Neo Enterprise engineering team.