Gen AI Engineer

Key Responsibilities: Agentic AI & RAG: Design multi-step workflows, autonomous AI agents, and retrieval pipelines connecting models to proprietary, real-time data. Model Fine-Tuning & Prompt Engineering: Optimize existing foundational models using Parameter-Efficient Fine-Tuning (PEFT) like LoRA/QLoRA and systematically refine system prompts. Evaluation & Guardrails: Implement robust validation, golden dataset evaluations, and output guardrails to manage hallucinations and ensure safe, compliant responses. Deployment: Containerize models and integrate them as REST APIs into production environments. Must-Have Skills & Tech Stack: Programming Languages: Advanced proficiency in Python. Orchestration Frameworks: LangChain, LangGraph, LlamaIndex, or Hugging Face. Vector Databases: Pinecone, ChromaDB, Weaviate, or pgvector. Cloud AI Platforms: Amazon Web Services (AWS Bedrock), Microsoft Azure (Azure OpenAI), or Google Cloud (Vertex AI). Concepts: Transformer architectures, tokenization, model context protocol (MCP), and MLOps.