GenAI Solution Architect
Job Summary
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.
Required Experience:
Staff IC
About Company
At Virtusa, we are builders, makers, and doers. Digital engineering is in our DNA. It’s at the heart of everything we do.