Experience in software engineering and enterprise architecture
Experience building AI/ML and Generative AI solutions
Role Summary
We are looking for a hands-on AI Architect who designs and builds enterprise‑scale Generative AI systems. This is not a conceptual or advisory role. You will stay close to the code, guide low-level implementation, review pull requests, and help teams solve real production challenges in GenAI systems deployed on Azure and AWS.
What You Will Do
• Actively design and build enterprise-grade GenAI solutions for real business use cases.
• Own end-to-end AI architecture, from model selection and retrieval strategy to APIs, orchestration, and cloud deployment.
• Write code and prototypes using Python, FastAPI, LangChain, LangGraph, and MCP.
• Design and implement RAG pipelines including chunking strategies, embeddings, vector search, and hybrid retrieval.
• Build and guide multi-agent systems with tool calling, memory/context management, and fallback handling.
• Dive into low-level technical details to unblock teams (retrieval quality, latency, hallucinations, cost issues).
• Make architecture-level trade-offs across accuracy, latency, scalability, reliability, and cost.
• Deploy and operate GenAI systems on Azure and AWS, ensuring scalability, resilience, and cost efficiency.
• Implement observability: logging, tracing, token usage tracking, and performance monitoring.
• Establish hands-on best practices for prompt versioning, evaluation, testing, and AI quality assurance.
• Implement guardrails and safety controls including content filtering, PII protection, hallucination mitigation, and access control.
• Design secure architectures using OAuth, Azure AD / IAM, RBAC, compliance, and audit logging.
• Review code, document architectural decisions, and mentor engineers by working alongside them.
What You Must Bring
• Strong hands-on experience with LLMs, RAG, Agentic AI, and prompt engineering.
• Proven ability to code, debug, and optimize production AI systems.
• Solid experience building GenAI solutions on both Azure and AWS (cloud-agnostic mindset).
• Hands-on experience with:
• Azure OpenAI, Claude or similar LLM platforms
• LangChain, LangGraph, MCP
• Redis, PostgreSQL, Cosmos DB, vector databases
• Deep understanding of distributed systems and cloud-native architecture.
• Ability to guide teams at implementation level, not just through design documents
