LLM RAG Pipeline Engineer

Responsibilities:

The role involves building and optimizing LLM-based pipelines, focusing on Retrieval Augmented Generation using tools like FAISS or Chroma. Responsibilities include prompt engineering, implementing rule-driven generation workflows, and ensuring schema enforcement and structured output control using Guardrails. Additionally, the role requires document parsing, chunking, and metadata design for retrieval workflows.

Requirements:

• Strong experience in Python development is essential, along with hands-on experience in building LLM-based pipelines using platforms such as Bedrock, OpenAI, or Anthropic.

Required Skills:

• Strong Python development experience.
• Hands-on experience building LLM-based pipelines (Bedrock/OpenAI/Anthropic).
• Retrieval Augmented Generation using FAISS or Chroma.
• Prompt engineering and rule-driven generation workflows.
• Experience with Guardrails for schema enforcement and structured output control.
• Document parsing, chunking, and metadata design for retrieval workflows.

Preferred Skills:

• Experience with LangChain or LlamaIndex.
• Familiarity with structured rule stores (JSON/YAML).
• Exposure to AWS model-oriented services (Bedrock runtime, Lambda for orchestration).
• Understanding of retrieval optimization and hallucination mitigation techniques.

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