Python GenAI Engineer

Capgemini's interview process typically starts with an online assessment covering quantitative aptitude, logical reasoning and coding. The technical interview focuses on data structures, algorithms, Python fundamentals and AI concepts like LLMs and prompt engineering. The final HR round evaluates cultural fit, communication skills and career aspirations. Preparing with mock tests and real‑world AI project examples greatly improves success chances.

Capgemini promotes a collaborative and inclusive culture where learning is encouraged through continuous upskilling and mentorship. Employees enjoy flexible hybrid work arrangements, a strong focus on work‑life balance, and clear career progression pathways.

B.Tech/B.E/M.Tech/M.Sc in Computer Science, Information Technology, Electronics or related streams; minimum 60% aggregate (or CGPA 6.5/10); graduating batch 2024‑2026; no active backlogs at the time of joining; strong programming fundamentals and a keen interest in AI/ML.

Round 1: Online Assessment (aptitude + coding) → Round 2: Technical Interview (AI concepts, Python, system design) → Round 3: HR Interview (fit, motivations, cultural alignment)

Capgemini is a global leader in consulting, technology services and digital transformation. With nearly six decades of experience, the firm serves more than 420,000 professionals across 50+ countries, helping clients re‑imagine their businesses through innovative solutions. The company’s strong focus on sustainability, diversity and responsible AI has earned it a reputation as a forward‑thinking employer that invests heavily in employee growth, certifications and continuous learning. Capgemini’s Indian operations, especially in technology hubs like Bangalore, are known for delivering cutting‑edge projects for Fortune‑500 clients in sectors such as banking, retail, manufacturing and healthcare. The role of Python GenAI Engineer is designed for fresh graduates who are passionate about generative AI and want to build real‑world applications that leverage large language models (LLMs). You will be part of an AI‑centric delivery team that creates end‑to‑end solutions, from prompt design to model fine‑tuning, and integrates them into enterprise‑grade platforms. This position offers exposure to the latest AI frameworks, cloud services and DevOps practices, making it an ideal launchpad for a career in AI engineering. **Key Responsibilities** 1. Design, develop and enhance generative AI applications using LLMs such as GPT‑4, LLaMA or Falcon. 2. Build Retrieval‑Augmented Generation (RAG) pipelines, manage vector stores and implement semantic search capabilities. 3. Craft effective prompts and fine‑tune models using LoRA, QLoRA or similar parameter‑efficient techniques. 4. Develop RESTful APIs to expose AI services and integrate them with existing enterprise applications. 5. Deploy solutions on cloud platforms (Azure, AWS, GCP) and containerise them using Docker/Kubernetes. 6. Implement CI/CD pipelines, monitor performance, and ensure high availability of AI services. 7. Write clean, testable Python code, conduct unit testing, debugging and code reviews. 8. Adhere to Responsible AI guidelines, data privacy regulations and security best practices. 9. Document architecture, APIs and usage guidelines for internal and client teams. 10. Stay updated with emerging AI research, tools and industry trends to continuously improve solutions. **Tech Stack**: Python, OOP, FastAPI/Flask, LangChain, LangGraph, LlamaIndex, Hugging Face, PyTorch, TensorFlow, Keras, Docker, Git, CI/CD, Azure/AWS/GCP, Vector DBs (Pinecone, Weaviate), RAG, LoRA/QLoRA. **Growth Path**: Starting as an Associate Engineer, you can progress to Senior Engineer, AI Solution Architect and eventually AI Practice Lead, with opportunities to lead cross‑functional projects, obtain specialized certifications and contribute to Capgemini’s AI thought‑leadership. **Why Join Capgemini?** The company offers a hybrid work model, robust learning budget, mentorship programs and exposure to global clients. Freshers gain hands‑on experience on production‑grade AI systems, receive guidance from seasoned AI experts and can accelerate their careers in a supportive, inclusive environment.