Senior AI Engineering Lead

4 days ago

Gandhinagar, Gujarat, India beBeeGenAi Full time ₹ 1,50,00,000 - ₹ 2,50,00,000

Job Title: Senior AI Engineering Lead

About the Role

We are seeking an experienced Senior AI Engineer to lead our GenAI and Prompt Engineering efforts. As a contractor, you will design intelligent assistants and agents to augment CI/CD workflows, knowledge retrieval, incident handling, and developer productivity using LLMs, Python, and NLP pipelines.

This is a high-profile role where you will work closely with DevOps and platform teams to drive automation with GenAI. You will be responsible for designing and optimizing prompts for LLM workflows, building RAG pipelines using vector DBs (FAISS, Pinecone, Weaviate, Qdrant), and integrating LLMs into internal tools, CI/CD, and observability dashboards.

Key Responsibilities
  • Design and optimize prompts for LLM workflows ensuring accuracy and relevance
  • Build RAG pipelines using vector DBs (FAISS, Pinecone, Weaviate, Qdrant)
  • Integrate LLMs into internal tools, CI/CD, and observability dashboards
  • Fine-tune open-source/commercial LLMs (OpenAI, Claude, Cohere, Mistral) for domain use cases
  • Collaborate with DevOps and platform teams to drive automation with GenAI
Requirements
  • 6-10+ years in engineering, with 2+ years hands-on in GenAI/LLMs/NLP
  • Strong Python skills with experience in LangChain, Hugging Face, LlamaIndex
  • Deep knowledge of vector databases and embeddings
  • Proven experience designing and deploying RAG architectures
  • Experience integrating LLM APIs (OpenAI, Azure OpenAI, Claude, etc.)
Bonus Skills
  • Experience with multi-modal models / fine-tuning LLMs
  • Familiarity with developer-facing GenAI use cases: infra-as-code review, changelog generation, log triage
  • Exposure to Kubernetes, GitOps, DevSecOps

The ideal candidate will have a strong background in engineering, with at least 6-10 years of experience, including 2+ years of hands-on experience in GenAI, LLMs, and NLP. Strong Python skills, particularly with LangChain, Hugging Face, and LlamaIndex, are essential. Additionally, the candidate should have deep knowledge of vector databases and embeddings, as well as proven experience designing and deploying RAG architectures.

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