Gen AI Engineer

Overview

On Site
Depends on Experience
Full Time

Skills

RAG
LLM
Python

Job Details

Role Overview:

As an LLM Engineer, you will be a key part of a GenAI transformation program, building intelligent, scalable LLM-powered solutions that enhance internal productivity, customer experiences, and operational decision-making. You will partner with business stakeholders, product owners, and data scientists to design and deploy use-case driven GenAI applications using Large Language Models and vector databases.

Key Responsibilities:

  • Design, fine-tune, and deploy LLM-based applications and copilots tailored for retail business workflows (e.g., customer service, merchandising, catalog intelligence).
  • Evaluate, prompt, and orchestrate LLM APIs (OpenAI, Anthropic, Mistral, etc.) in enterprise-ready environments.
  • Build secure RAG (Retrieval-Augmented Generation) pipelines using enterprise-grade unstructured content.
  • Integrate vector databases (e.g., FAISS, Weaviate, Milvus, Pinecone) for semantic search and context injection.
  • Develop backend services (Python/FastAPI/Flask) for scalable LLM app deployment.
  • Collaborate with DevOps/Data Engineers to deploy LLM workloads in cloud/on-prem environments (Azure/Google Cloud Platform).
  • Ensure enterprise compliance on data security, PII masking, token limits, audit logging, and rate-limiting.
  • Tune performance using evaluation frameworks like RAGAS, Promptfoo, LLM Benchmarks, etc.
  • Translate business use cases into rapid LLM POCs and scale MVPs to production.

Required Technical Skills:

  • Strong programming in Python with experience in LangChain, LlamaIndex, or Haystack.
  • Proven experience with OpenAI, Azure OpenAI, Cohere, or other LLM APIs.
  • Proficiency in vector database integration: FAISS, Weaviate, Milvus, or Pinecone.
  • Understanding of prompt engineering, prompt chaining, function calling, and tool use with LLMs.
  • Familiarity with REST APIs, FastAPI, and containerization (Docker, Kubernetes).
  • Good grounding in MLOps/DevOps for deploying models in secure environments.
  • Working knowledge of front-end integration (React/Streamlit) is a plus.

Preferred Qualifications:

  • 5 8 years of experience in AI/ML/NLP with 1+ years focused on LLMs.
  • Experience working in the retail domain or building GenAI solutions for enterprise clients.
  • Exposure to unstructured document processing, OCR, or knowledge extraction is a plus.
  • Bachelor's or Master's in Computer Science, AI, Data Science, or related field.
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