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3 days ago
This is a highly challenging and rewarding role for a seasoned GenAI professional with expertise in Prompt Engineering, Retrieval-Augmented Generation (RAG), and Vector Search Systems. You will be responsible for integrating GenAI into our engineering ecosystem.
Key Responsibilities:- Design and optimize prompts for LLM workflows to ensure accuracy and relevance.
- Build RAG pipelines using vector databases (FAISS, Pinecone, Weaviate, Qdrant).
- Integrate LLMs into internal tools, CI/CD, and observability dashboards.
- Implement GenAI solutions using LangChain, LlamaIndex, Haystack, Hugging Face, etc.
- Fine-tune open-source/commercial LLMs (OpenAI, Claude, Cohere, Mistral) for domain use cases.
- Collaborate with DevOps teams to drive automation with GenAI.
- Ensure data privacy, governance, and ethical AI practices.
- 6-10+ years of experience 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.).
- 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.
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