• This role sits at the intersection of traditional software engineering and machine learning.

• The primary goal is to solve complex enterprise problems by securely integrating AI endpoints into existing systems.

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.