GenAI Solutions Architect & Engineering Leader
Enterprise AI Architect with 11+ years of experience engineering secure, compliant, and highly available systems for global enterprises.
Systems & Agentic Workflows: I build multi-agent orchestration engines (Agent OS), advanced RAG platforms, and target-fine-tuned models.
Core Engineering: Deep expertise in LangGraph, Neo4j Graph RAG, vector search engines, Python/Go backend systems, and Kubernetes.
Enablement & Consulting: Hands-on systems design, structured AI training for engineering teams, and code-level developer mentorship.
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I am Prasant Mishra, an Enterprise GenAI Solutions Architect and engineering leader. Over the past 11+ years, I have built production-grade AI platforms, engineered low-latency backends, and trained engineering teams at global organizations including IBM Labs, Intel, ABInBev, and Adobe.
I specialize in designing and delivering resilient enterprise architectures across three core offerings:
Deconstruct business objectives and metrics before selecting tooling.
Architect decoupled systems with compliance and ingestion guardrails.
Build tested codebases equipped with offline evaluations and telemetry.
Deliver runbooks, workshops, and coaching for team independence.
Programming Languages
Core language for AI engineering, backend systems, automation, and rapid prototyping.
Helping organizations accelerate AI adoption, modernize backend services, and elevate internal engineering capabilities.
Designing and delivering resilient, production-ready backend microservices, database systems, and agentic AI orchestration architectures.
Designing and executing rigorous quality evaluation frameworks, metric benchmarks, and offline test-harness pipelines for RAG systems.
Creating custom training platforms, interactive playground sandboxes, and structured frameworks that accelerate AI up-skilling.
Delivering interactive workshops, structured lectures, and customized corporate AI up-skilling programs, both online and offline.
A showcase of open-source contributions, enterprise platforms, and deep system-design implementations.
An evaluation-first Python library for LLM and RAG workflows, designed to make quality checks, benchmark creation, and comparisons more practical and repeatable.
Delivered retrieval-grounded pipelines for code explanation and transformation workflows with hybrid search, reranking, and enterprise-ready ingestion.
Developed a generation-and-evaluation pipeline for COBOL-to-Java modernization, using verified data pairs and targeted fine-tuning for continuous improvement.
Orchestrated an enterprise Knowledge Graph platform utilizing Neo4j, implementing optimized multi-hop graph-traversal queries to resolve IP asset dependencies.
Architected a unified Azure MLOps and lakehouse platform on Azure Databricks, orchestrating continuous model deployment and standardized feature pipelines.
Designed a real-time event-driven telemetry and change-data-capture synchronization pipeline handling high-throughput messaging queue monitoring.
Measured outcomes across enterprise AI systems, platform modernization, and MLOps engineering.
Elevated retrieval precision through hybrid search index tuning, layout classification, and reranking.
Architected multi-agent pipelines (Agent OS) and automated code modernization tools for enterprise deployment.
Standardized model lifecycles and feature store pipelines across Databricks and Domino MLOps platforms.
Engineered Celery/RabbitMQ telemetry systems, shifting operations from reactive triage to proactive queue-based monitoring.
Developer tutorials, system design breakdowns, and courses on modern AI engineering. Read my latest publications across different engineering tracks.
A step-by-step breakdown of managing complex agent states, cyclic flows, validation, and human-in-the-loop controls using LangGraph.
Read on Qurious Academy →How to evaluate enterprise retrieval systems by mapping answers back to raw document offsets to diagnose retrieval failures accurately.
Read on Qurious Academy →Exploring concurrency patterns, cache validation layers, and optimized connection pools for enterprise-level backend servers.
Read on Qurious Academy →Extending LLM capabilities by building tailored MCP servers that expose enterprise database tooling and APIs securely.
Read on Qurious Academy →Leveraging Neo4j knowledge graphs and vector engines together to retrieve both structured relationships and semantic context.
Read on Qurious Academy →Managing complex agent states, cyclic flows, state-machine validation, and human-in-the-loop validation using LangGraph.
Span-anchored evaluation, automated test generation, custom judges, and integrating evaluation gates into CI/CD pipelines.
Upskilling developers, setting up local playgrounds, designing code validation tools, and setting up guardrails.
Deploying models securely under strict data compliance, private networks, vector search scale, and high-performance middleware design.
Custom GenAI system-design blueprints, RAG evaluations, security audits, and multi-agent workflows integration.
Developer upskilling programs, model fine-tuning workshops, prompt engineering, and isolated sandbox lab setups.
Resilient microservices engineering (FastAPI/Go), database pipelines, and high-availability Kubernetes deployments.