AI Engineer
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Job Description
- Strong expertise in RAG architecture and implementation, including
- Data ingestion pipelines
- Document chunking strategies
- Embeddings and semantic retrieval
- Hybrid search and reranking
- Evaluation metrics and hallucination mitigation
- Experience with vector databases such as Pinecone/FAISS/Weaviate
- Hands-on experience with LLM APIs (OpenAI, Anthropic, Azure OpenAI) and prompt orchestration.
- Proficiency in LangChain, LlamaIndex, or equivalent frameworks for conversational workflows and RAG applications.
- Experience integrating AI solutions with APIs, enterprise systems, and automation workflows.
- Exposure to multi-agent orchestration, tool-calling, and autonomous workflows preferred but not mandatory.
- Lead and mentor a team of 3–4 engineers delivering customer-facing AI initiatives.
- Drive discovery, solution design, and deployment of GenAI and RAG use cases.
- Own innovation roadmap for AI offerings within EIRS.
- Work with stakeholders to identify new opportunities for knowledge assistants, copilots, document intelligence, and AI automation.