Building
AgentsPipelinesSystemsModelsAgentsSystems
that run in production

AI / ML Engineer, Agentic Systems Engineer, and MLOps / Platform roles where I can own the model-orchestration layer and the production infrastructure beneath it. I'm happiest building agentic pipelines, NLQ/RAG systems, and the automation that ships them reliably.

Yes. I'm based in Bangalore, India and open to hybrid, remote, and relocation for the right team, including global opportunities working across time zones.

I architect for reliability, not demos: 750+ CI/CD deployments, 12+ microservices serving 500+ organizations, 60% API performance gains through query and indexing work, and MLOps pipelines that handle deployment, monitoring, and lifecycle management end-to-end.

The whole pipeline: multi-agent orchestration (LangGraph, LangChain, AutoGen), retrieval and embeddings, prompt engineering and RLHF feedback loops, plus the cloud-native infrastructure (AWS, Azure, Docker, Kubernetes) to run it in production.

By treating autonomy as an engineering property, not a prompt trick. I build agents on LangGraph with typed tools and deterministic skills, fan-out routing for parallel work, and explicit guards around every model call: timeouts, retries, and schema validation. Reasoning steps are logged and evaluable, so failures isolate and debug cleanly instead of turning mysterious. That's how RapidDoc's multi-agent pipeline indexes large monorepos, and how Smart Log Manager pins root cause across noisy multi-tenant logs.

End-to-end ownership: containerized model serving on Kubernetes / OpenShift, automated deployment and rollback, monitoring for drift and latency, and RLHF feedback loops that fold real usage back into evaluation. At HPE I build the pipelines that handle deployment, monitoring, and lifecycle management across hybrid-cloud environments, so a model that improves in a notebook actually reaches users safely.

I scaled a B2B health-tech ERP to 500+ organizations, so tenant isolation and performance were daily constraints. That meant careful data partitioning, indexing and query tuning that cut API latency 60%, and 750+ CI/CD deployments kept safe with automated tests and 50% fewer regressions. I design for failure modes first: isolation, observability, graceful degradation. Then optimize.

Yes. I was Principal Author on a published blockchain-based recommendation system (ICT4SD 2025) and have placed in multiple national hackathons for AI and distributed-systems work.

I'm available to talk now. The fastest way to reach me is the contact form below or email directly. I usually respond within 24 hours. You can also grab my one-page résumé from the top of the page.