GenAI Solution Architect

Role Overview:

• 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.