Enterprise AI Systems Architect

Manoj Mukherjee

ArchitectingProduction-GradeAI Systems

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.

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Best fit for: AI architecture audits, LangGraph orchestration consulting, RAG reliability reviews, fractional AI architect retainers, and DevRel engineering partnerships.

Production AI Architecture

Design → Operate → Scale

01
Strategy
Business workflow
Define operational workflows, user journeys, constraints, and measurable success criteria before selecting models or orchestration patterns.
02
RAG
Retrieval layer
Design corpus strategy, chunking pipelines, hybrid retrieval, pgvector indexing, memory systems, and grounding architecture.
03
Agents
Agent runtime
Model LangGraph state transitions, tool permissions, retries, checkpoints, memory boundaries, and human approval gates.
04
Backend
AI backend
Expose FastAPI services, async workers, queues, traces, observability pipelines, and integration contracts for production workloads.
05
Reliability
Evaluation loop
Continuously track answer quality, retrieval regressions, hallucination patterns, latency, token cost, and operational failure modes.
06
Platform
Deployment path
Deploy across cloud infrastructure, containers, model runtimes, observability systems, CI/CD pipelines, and operational handoff workflows.

10+

years in Systems & AI Engineering

50+

AI architectures reviewed

AI L2

Publicis Sapient AI Engineer certification

2.8K

LinkedIn technical audience

Production Proof

Enterprise AI credibility, grounded in delivery.

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

Premium AI architecture work for technical buyers.

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.

AI Architecture Advisory

I provide senior architecture guidance for teams moving AI products from vague ambition to a production-ready operating model.

Buyer pain

AI roadmap is moving faster than the architecture decisions behind it.

Clarify system boundaries, platform choices, and delivery risk
Define POC-to-production roadmap for AI-native products
Review reliability, governance, evaluation, and deployment strategy

LangGraph Consultant

I help you design agentic workflows with explicit state, deterministic routing, short/long-term memory, robust fallbacks, and human-in-the-loop validation gates.

Buyer pain

Agent workflows work in demos but become opaque, brittle, or hard to debug in production.

Model agent workflows as debuggable state machines
Reduce brittle tool-calling and hidden orchestration behavior
Build evaluation paths for multi-agent reliability

RAG Infrastructure Consulting

I optimize retrieval quality, precision grounding, querying latency, and user trust for knowledge-heavy, vector-driven AI systems.

Buyer pain

Retrieval quality, grounding, latency, or hallucination risk is blocking trust.

Design hybrid retrieval and pgvector indexing strategy
Improve chunking, ranking, grounding, and answer quality
Create evaluation datasets and regression loops for RAG

View All Consulting Services

Engineering Authority

The work is architecture, not AI theater.

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.

Multi-Agent Systems

LangGraph-based workflows with explicit state, tool routing, memory, fallbacks, and evaluation.

01
Intent
02
Planner
03
Agent State
04
Tools
05
Human Gate
06
Trace

Tradeoffs

state visibilitytool safetyretry behaviorhuman control

RAG Reliability

Retrieval pipelines designed for grounding quality, latency budgets, observability, and regression testing.

01
Corpus
02
Chunking
03
pgvector
04
Hybrid Search
05
Rerank
06
Evals

Tradeoffs

chunkingrankinggroundinglatency

FastAPI AI Backends

Async Python services, model gateways, queues, trace IDs, and deployment paths for AI product teams.

01
API
02
Queue
03
Workers
04
Model Gateway
05
Store
06
Observability

Tradeoffs

async workloadsAPI contractscost controlsdeployment

Explore Engineering Details & Decision Map

Work With Me

Need an architecture review before AI decisions harden?

Use the advisory intake for RAG quality, agent reliability, platform backend, deployment, observability, or DevRel architecture questions.

Start Advisory Intake View GitHub

Technical Surface

A modern AI platform stack, grounded in delivery.

Hands-on across the ecosystem needed to move from POC to production: orchestration, retrieval, backend services, deployment, and developer education.

LangGraphGoogle ADKMCP / ACP / UCPAgentic RAGpgvectorFastAPINext.jsVertex AIAWS / GCPNVIDIA Run:AIOpenShiftKubernetesvLLMOllamaAI observability

Career Journey

From product engineering to AI systems architecture.

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

Technical Lead / AI Architect

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.

LangGraphAgentic RAGpgvectorFastAPIKubernetes

2022-2023

Chief Manager (SDE III)

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.

React NativeTypeScriptUPISecurityArchitecture

2018-2022

Enterprise Platform Engineering

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.

Next.jsModule FederationGraphQLMicrofrontendsCloud

2016-2018

Software Engineer

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.

ReactRedux SagaNode.jsExpressAnalytics

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Trust

People I’ve worked closely with.

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.”
Soumya Ghatak
Senior Program & Product Manager
MentorArchitecture clarity

Writing

Technical content for senior builders.

Writing focuses on AI infrastructure, local agentic workflows, LangGraph systems, and practical implementation choices.

AI infrastructure / April 2026

Desktop AI Supercomputing is Here: A Practical Look at NVIDIA DGX Spark for Startups

Multi-agent systems / May 2025

The Future of AI: Building Agent-to-Agent Communication Systems

LangGraph / January 2025

Building an AI-Powered Stock Analysis Pipeline with LangGraph, DeepSeek, and Ollama

Read All Writing

Work With Me

Bring the AI system constraint.

If the challenge involves LangGraph orchestration, RAG infrastructure, FastAPI AI backends, AI platform engineering, or technical DevRel, start with the system context.

Start Advisory Intake View GitHub