Enterprise AI Systems Architect
I help CTOs, AI startups, and platform teams design reliable multi-agent systems, enterprise RAG infrastructure, and FastAPI AI backends.The focus is production-grade AI systems built for real-world scale, latency, reliability, and governance constraints.
Best fit for: AI architecture audits, LangGraph orchestration consulting, RAG reliability reviews, fractional AI architect retainers, and DevRel engineering partnerships.
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10+
years in Systems & AI Engineering
50+
AI architectures reviewed
AI L2
Publicis Sapient AI Engineer certification
2.8K
LinkedIn technical audience
Production Proof
The strongest signal is not audience size. It is the ability to connect AI workflows, retrieval systems, backend services, deployment constraints, and engineering adoption into one production path.
Enterprise AI platforms
Agentic RAG, multi-agent orchestration, and AI-native workflows for enterprise adoption.
Retrieval infrastructure
Hybrid search, pgvector patterns, contextual retrieval, and long-term memory pipelines.
AI backend systems
FastAPI microservices, async Python, typed APIs, deployment topology, and observability paths.
Platform delivery
Kubernetes, OpenShift, Vertex AI, NVIDIA Run:AI, vLLM, Ollama, and cloud-native execution.
Services
The offer is not generic implementation help. It is architecture, reliability, platform design, and technical market credibility for teams where AI has become a product and infrastructure problem.
Engineering Authority
A credible AI platform needs more than a model call. It needs retrieval quality, state management, evaluation, deployment constraints, failure handling, and observability designed from the start.
LangGraph-based workflows with explicit state, tool routing, memory, fallbacks, and evaluation.
Execution Pipeline
Architectural Tradeoffs
Retrieval pipelines designed for grounding quality, latency budgets, observability, and regression testing.
Execution Pipeline
Architectural Tradeoffs
Async Python services, model gateways, queues, trace IDs, and deployment paths for AI product teams.
Execution Pipeline
Architectural Tradeoffs
Work With Me
Use the advisory intake for RAG quality, agent reliability, platform backend, deployment, observability, or DevRel architecture questions.
Technical Surface
Hands-on across the ecosystem needed to move from POC to production: orchestration, retrieval, backend services, deployment, and developer education.
Multi-agent platforms, context coordination protocols, tracing suites, and visual workflows.
Enterprise backend engines, asynchronous API runtimes, and local browser/Node ML execution.
Component layout matrices, lightweight reactive state nodes, and design system libraries.
Relational cloud SQL, vector embedding stores, key-value databases, and semantic Graph DBs.
Cloud-native compute engines, enterprise GenAI bedrock services, storage clusters, and access control.
Edge networks, hosting providers, AI developer assistants, package/container registries, and runner pipelines.
Career Journey
The AI architecture position is built on a decade of production work: product surfaces, enterprise platforms, regulated systems, cloud-native delivery, and now agentic AI infrastructure.
2023-Present
Publicis Sapient
I lead the architecture of enterprise-grade generative AI platforms using Agentic RAG, stateful LangGraph-style orchestration, hybrid retrieval, FastAPI microservices, and containerized deployment paths.
Production signal
I design production AI systems optimized for retrieval precision, structured agent state, observability, low latency, token costs, and engineering team adoption.
2022-2023
Kotak Mahindra Bank
I operated closer to core architecture and solution design, directing research and development, vendor evaluations, and high-stakes banking platform reviews for the Kotak811 digital banking platform.
Production signal
I developed a strong judgment around regulated banking constraints, secure API design, multi-stakeholder alignment, and critical CTO-level architecture tradeoffs.
2018-2022
HPE, Optiv, Krista, Maersk
I moved from standard application delivery to platform engineering. I built hybrid cloud UIs, cybersecurity dashboards, and automation tooling while designing microfrontends, server-side rendering patterns, and sharing components across enterprise design systems.
Production signal
I worked across scale enterprise environments where architecture decisions directly impacted developer onboarding, release cadences, system maintainability, and operational confidence.
2016-2018
William O'Neil India
I joined as a founding member of the India engineering team, building Panaray, a flagship financial research and stock market analytics web platform. I designed state management patterns with Redux Saga and built Node.js BFF services from scratch.
Production signal
I shipped customer-facing financial analytics software where reliability, high-frequency market data workflows, and rendering performance were critical.
Trust
Thoughts from engineers, leaders, and collaborators across production systems, architecture, and platform engineering.
LinkedIn recommendations
01 / 08
“Manoj consistently demonstrates exceptional dedication, intellectual rigor, and the ability to translate complex problems into practical, implementable solutions.”
Blog
The blog focuses on AI infrastructure, local agentic workflows, LangGraph systems, and practical implementation choices.
AI infrastructure / April 2026
Multi-agent systems / May 2025
LangGraph / January 2025
Work With Me
If the challenge involves LangGraph orchestration, RAG infrastructure, FastAPI AI backends, AI platform engineering, or technical DevRel, start with the system context.