Capgemini - Generative AI Engineer in India, भारत

GenAI EngineerJob Description :GenAI Engineers will build and deliver components of Generative AI and Agentic AI solutions, leveraging Large Language Models (LLMs) and Small Language Models (SLMs) across enterprise use cases. Working within a broader project team and under the guidance of senior engineers and architects, the GenAI Engineer implements well-defined features and technical designs, experiments with new GenAI techniques, and builds strong hands-on depth in fine-tuning and deploying LLM/SLM-based solutions. The role calls for curiosity, a strong learning mindset, and the ability to independently execute well-scoped https://jobeax.com/link/drx7bmkJv6r8wCyv Requirements :Education :- Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a related https://jobeax.com/link/nnF4EyWTLvU0XkJo :- 5+ years of experience in AI/ML engineering, including at least 1 to 2 years of hands-on experience building GenAI/LLM-based applications and SLM fine tuning to create specialized models.- Experience contributing to at least one GenAI use case through to production deployment as part of a project team.- Experience on specialized model training and deployment on sovereign/local on premise https://jobeax.com/link/n7EY6UfK03BNs3cq :Required Skills :- Hands-on experience fine-tuning and adapting LLMs and SLMs using standard approaches, including LoRA, QLoRA, PEFT, and instruction tuning, under the direction of senior engineers or architects.- Working knowledge of commonly used LLMs (e.g., GPT-4/4o, Claude, Gemini, Llama, Mistral) and SLMs (e.g., Phi, Gemma, Mistral-7B) for well-defined enterprise use cases.- Proficiency in Python and GenAI development frameworks such as LangChain, LlamaIndex, Semantic Kernel, Haystack, AutoGen, and CrewAI.- Practical experience with prompt engineering, prompt chaining, and few-shot/zero-shot techniques.- Experience implementing components of Agentic AI systems, including tool/function calling and agent workflows, based on architecture and patterns defined by senior team members.- Experience building Retrieval-Augmented Generation (RAG) pipelines, including chunking, embedding models, and vector databases (e.g., Pinecone, Weaviate, FAISS, Chroma, Milvus).- A strong learning and innovation mindset, with the ability to experiment with new GenAI techniques and bring ideas to the team.- Good analytical and problem-solving skills, with the ability to independently execute well-scoped tasks and communicate progress https://jobeax.com/link/TTaWhNlDysxq3SE3 Skills :- Familiarity with responsible AI practices, including guardrails, hallucination mitigation, and bias detection.- Exposure to cloud GenAI platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AI.- Basic knowledge of MLOps/LLMOps practices, including CI/CD for ML, model versioning, and monitoring tools (e.g., LangSmith, Weights & Biases).- Understanding of containerization tools (e.g., Docker) for packaging GenAI services.- Exposure to building or consuming REST/GraphQL APIs for GenAI capabilities.- Familiarity with knowledge graphs and semantic https://jobeax.com/link/4R31UdXrfPSGXBJl :- Develop and fine-tune GenAI/Agentic AI components using LLMs/SLMs against defined technical designs.- Build and support RAG pipelines, vector search, and retrieval components.- Implement agent workflows, including tool use and task execution, based on architecture defined by senior engineers.- Assist in fine-tuning and optimizing LLMs/SLMs under the guidance of senior team members.- Contribute to the delivery of assigned GenAI features within a broader project team, from build through deployment.- Collaborate with senior engineers, data engineers, and architects to implement solution requirements.- Support the evaluation of new GenAI tools and frameworks.- Apply guardrails and evaluation checks as defined by the team's responsible AI practices.- Test and validate GenAI solution components for accuracy and performance.- Stay current with GenAI, LLM, SLM, and Agentic AI developments and share learnings with the team. (ref:hirist.tech)