AI Solutions Architect - Agentic AI Platforms

• Agentic AI Architecture: Own end-to-end design of multi-agent systems using LangChain, LangGraph, and Model Context Protocol (MCP) - including planner-executor patterns, sub-agent hierarchies, tool routing, retries, and cost-aware token budgeting.
• RAG & Knowledge Systems: Architect production-grade RAG pipelines with vector databases (pgvector, Qdrant), hybrid retrieval, re-ranking, and document-aware chunking to ground agents in enterprise knowledge.
• Solution Architecture: Design reference architectures and solution blueprints for enterprise clients across BFSI, payments, manufacturing, and government - translating business outcomes into agentic AI roadmaps and reusable accelerators.
• Scalable Microservices: Build event-driven microservices on Kafka, polyglot data layers with PostgreSQL and vector DBs, and Kubernetes-based deployment topologies for high-throughput inference workloads.
• MLOps & Model Lifecycle: Establish practices spanning training, fine-tuning, prompt and config versioning, structured evaluations against golden datasets, drift detection, and automated rollback when output quality degrades.
• Traceability & Observability: Instrument agent reasoning traces, tool-call audit trails, token spend, and quality signals with Prometheus, Grafana, and Open Telemetry - enabling policy enforcement and human-in-the-loop oversight.
• Reusable Engineering Standards: Codify AI engineering patterns (RAG retrievers, agent loops, eval harnesses, traceability spans) into reusable skills, sub-agents, and platform components consumed across multiple product lines.
• Rapid Engineering in ADLC: Roll out AI-led developer tools and sub-agents (Claude Code, Playwright MCP) across planning, code generation, code review, test authoring, and release validation - accelerating delivery while standardizing quality.
• Presales & Client Engagement: Partner with sales, presales, and customer success on enterprise pursuits - authoring solution designs, leading technical workshops, and shaping agentic AI roadmaps for prospects and existing clients.

• 10+ years of software engineering experience, with at least 3 years architecting LLM-based or agentic AI systems in production.
• Deep hands-on expertise with LangChain, LangGraph, RAG, MCP, prompt engineering, context engineering, and token optimization.
• Strong programming skills in Python and TypeScript (Java a plus); proven ability to design and implement microservices with FastAPI, Spring Boot, or Node.js.
• Production experience with cloud platforms (AWS, Azure, or GCP), Kubernetes, Docker, Terraform, and CI/CD pipelines.
• Solid grounding in MLOps - model training/fine-tuning, evaluation pipelines, drift detection, and observability for AI systems.
• Track record of solution architecture for enterprise clients - translating business problems into reference architectures and shipping production outcomes.
• Bachelor's degree in Computer Science, Engineering, or a related field.

NICE TO HAVE

• Experience in Retail or regulated industries (PCI-DSS, KYC/AML, audit & compliance workflows).
• Familiarity with API gateways (Apigee, Kong) and developer-portal design for partner onboarding.
• Exposure to Claude Code, Cursor, or similar agentic coding environments and the design of reusable skills/sub-agents