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3 days ago
We are seeking a seasoned expert to join our team as a GenAI Engineer. This is a highly specialized role that requires deep expertise in Prompt Engineering, Retrieval-Augmented Generation (RAG), and Vector Search Systems.
The ideal candidate will have extensive experience with integrating GenAI into engineering & DevOps ecosystems, designing intelligent assistants & agents to augment CI/CD workflows, knowledge retrieval, incident handling, and developer productivity using LLMs, Python, and NLP pipelines.
Key Responsibilities:- Design & optimize prompts for LLM workflows ensuring accuracy & relevance.
- Build RAG pipelines using vector DBs (FAISS, Pinecone, Weaviate, Qdrant).
- Integrate LLMs into internal tools, CI/CD & 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 & platform teams to drive automation with GenAI.
- Ensure data privacy, governance & ethical AI practices.
- 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 & embeddings.
- Proven experience designing & 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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