AI Engineer

Round 1: Online assessment (aptitude + coding) → Round 2: Technical interview (system design, AI/ML concepts, coding) → Round 3: HR interview (fit, communication, expectations)

Capgemini promotes a collaborative, inclusive environment where continuous learning is encouraged. Employees benefit from clear career ladders, global mobility options and a balanced work‑life approach supported by flexible work arrangements.

Capgemini's interview process typically starts with an online assessment that tests quantitative aptitude and coding ability, followed by a technical interview focusing on system design, AI/ML concepts and problem‑solving. The final HR round evaluates cultural fit and communication skills. Candidates should practice coding under time pressure, review LLM fundamentals and be ready to discuss real‑world AI deployment challenges.

B.Tech/B.E. or M.Tech in Computer Science, Information Technology, Electronics & Communication or related streams with a minimum of 60% aggregate (or CGPA 6.5/10). Must belong to the 2025 graduating batch. Maximum of 2 backlogs allowed (no active backlogs at the time of joining). Strong programming fundamentals and a passion for building production AI systems are essential.

Capgemini is a global leader in consulting, technology services and digital transformation. With a heritage of over 55 years, the firm operates in more than 50 countries and employs over 420,000 professionals. Its Indian operations are among the largest, delivering end‑to‑end solutions across banking, insurance, healthcare, manufacturing and many other sectors. The company’s culture is built around "unleashing human energy through technology", emphasizing inclusivity, sustainability and continuous learning. Capgemini’s AI practice combines deep domain expertise with cutting‑edge research to help clients adopt responsible, production‑grade generative AI solutions. The AI Engineer role in Pune is a hands‑on, builder‑focused position. You will be responsible for taking a concept—such as an LLM‑driven assistant or an autonomous agent—and turning it into a reliable, scalable service that runs in production under real‑world load. This is not a research or prototype role; you will own the end‑to‑end lifecycle, from problem framing and prompt engineering to deployment, monitoring, and post‑launch optimisation. You will work closely with senior engineers, product owners and data scientists to ensure that AI components integrate seamlessly with existing APIs, data pipelines and orchestration layers. **Key Responsibilities** 1. Design and implement production‑grade AI/agentic systems, handling architecture, deployment, monitoring and iteration. 2. Own the full lifecycle of at least one non‑trivial AI capability, including problem definition, evaluation strategy, prompt/context engineering, orchestration and post‑launch tuning. 3. Build deterministic guardrails around probabilistic models – retries, validation, fallback paths and human‑in‑the‑loop checkpoints. 4. Create robust evaluation harnesses and offline/online pipelines to detect regressions and ensure reliability. 5. Make architectural trade‑offs on latency, cost and reliability, factoring token economics from the outset. 6. Integrate AI services with existing production infrastructure (REST APIs, event‑driven systems, queuing, CI/CD). 7. Provide technical push‑back on unrealistic proposals with data‑driven reasoning. 8. Collaborate with cross‑functional teams to document design decisions, failure modes and mitigation strategies. 9. Mentor junior engineers on best practices for building and maintaining AI systems in production. 10. Stay abreast of the latest advancements in LLMs, agent frameworks and retrieval‑augmented generation, and evaluate their applicability to client problems. **Tech Stack**: Python, TypeScript, FastAPI/Node.js, Docker, Kubernetes/OpenShift, AWS/GCP/Azure, LLM APIs (OpenAI, Anthropic), agent frameworks (LangGraph, CrewAI, Strands), vector stores (Pinecone, Milvus), CI/CD tools (Jenkins, GitHub Actions), monitoring (Prometheus, Grafana). **Growth Path**: Successful engineers can progress to Senior AI Engineer, AI Solutions Architect, or AI Practice Lead, with opportunities to lead large‑scale AI programmes for Fortune‑500 clients and contribute to Capgemini’s internal AI research labs. **Why Join Capgemini**: You will work on mission‑critical AI products that impact real businesses, gain exposure to a global client base, and benefit from a structured learning ecosystem that includes certifications, mentorship and a vibrant community of AI practitioners. The role offers competitive compensation, comprehensive benefits and a culture that values direct, low‑ceremony communication, enabling you to make a tangible impact from day one.