Enterprise GenAI
Secure systems grounded in governed enterprise context.
Enterprise AI Architect · GenAI · Agentic AI · MLOps
I architect enterprise AI—from strategy to production.
I design secure, governed AI platforms where agents, enterprise context, models and operations work as one—turning ambitious AI programs into measurable outcomes.
- AI Strategy & Governance
- Agentic Platforms
- Context Fabric & RAG
- ML & LLMOps
14.5+ yearsEnterprise engineering & architecture
~50% lower MTTRThrough contextual AI developer tooling
CKAD certifiedCloud-native production delivery
Senior Consulting ManagerDewasheesh RanaAI Architect · Kolkata, India
Enterprise AI blueprint Vendor-neutral · 2026
01 · ExperienceUsers, apps & APIsChannels · Events · Copilots
02 · AccessAI gatewayIdentity · Policy · Routing
03 · AutonomyAgents & workflowsPlans · Tools · Memory · HITL
04 · KnowledgeRAG & contextSearch · Graph · Provenance
05 · IntelligenceModel & ML fabricGenAI · Predictive ML · Inference
06 · FoundationData & platformGoverned · Portable · Operable
Agentic Systems
Agents that reason, use tools and collaborate with people.
Production MLOps
Reliable delivery from experiment through monitoring.
Strategy & Governance
Roadmaps, operating controls and value-led AI portfolios.
Enterprise AI reference architecture · 2026
One reusable blueprint for GenAI, agents and machine learning.
A vendor-neutral capability map for enterprises that need choice, controlled autonomy and production discipline. Adopt the whole platform or compose only the layers a use case needs.
Business value & operating model
- Use-case portfolio & KPIs
- Product teams, ownership & change
- Funding, FinOps & value realization
Experiences & integration
- People, copilots & decisioning
- Apps, APIs, events & multimodal
- Edge, IoT & partner ecosystems
AI gateway & access
- Identity, tenants, budgets & quotas
- Policy, consent & guardrails
- Model-aware routing, cache & fallbacks
Agents & workflow runtime
- Planning, orchestration & multi-agent
- Tools, MCP & action gateway
- Identity, memory, state & human approval
Knowledge & context fabric
- Hybrid RAG, retrieval & re-ranking
- Ontology, knowledge & context graphs
- Document intelligence & provenance
Model & ML fabric
- Model catalog, gateway & routing
- Foundation, domain & classical ML
- Training, tuning, inference & optimization
Enterprise data foundation
- Lakehouse, warehouse & streams
- Operational systems, APIs & data products
- Quality, lineage, metadata & features
Platform & delivery
- Kubernetes, serverless, GPU & AI gateways
- Multi-cloud, on-prem & edge
- IaC, GitOps, CI/CD/CT & software supply chain
Operating lifecycleEvidence flows with every release
- Discover & assess
- Build & ground
- Evaluate & approve
- Deploy & observe
- Improve or retire
Designed as an enterprise platform product: reusable golden paths, self-service capabilities, isolated tenants, chargeback and auditable controls—without locking teams to one cloud or model provider.
Selected work
Architecture that connects intelligence to outcomes.
Representative initiatives spanning context-aware GenAI, process intelligence, developer tooling and production ML platforms.
01Architecture case
Process Intelligence & Root Cause Assistant
An AI-assisted investigation layer that connects process-mining signals, operational telemetry and precedent context to accelerate root-cause analysis.
Architecture signalProcess mining · Context fabric · LLM-assisted RCA
02Architecture case
Enterprise GenAI Context Fabric
A governed retrieval and reasoning platform combining hybrid search, knowledge graphs and agent workflows for context-aware enterprise decisions.
Architecture signalHybrid RAG · Knowledge graph · Agent orchestration
03Architecture case
AI Developer Copilot
A contextual engineering assistant for code, logs and API documentation, with auditable tools for operational replay, retry and investigation workflows.
Architecture signal~50% developer MTTR reduction · Tool calling · Secure retrieval
04Architecture case
Production MLOps Platform
A repeatable production path for training, tuning, serving and observing machine-learning systems on AWS EKS and cloud-native infrastructure.
Architecture signalAutomated delivery · Scalable serving · Model observability
Expertise
A full-stack view of production AI.
Architecture choices stay connected—from models and data to cloud platforms, delivery pipelines, governance and operations.
AI architecture
- Enterprise GenAI
- Agentic systems
- RAG architecture
- MCP & tool calling
- Context engineering
- AI governance
ML & LLM operations
- Kubeflow Pipelines
- Katib & KServe
- SageMaker
- MLflow & Evidently
- LLM / RAG evaluation
- Production observability
Cloud & platforms
- AWS & Azure
- EKS & Kubernetes
- Docker & Helm
- Terraform & CDK
- GitHub Actions
- Event-driven systems
Engineering & data
- Python & FastAPI
- Java & Spring Boot
- Qdrant & FAISS
- OpenSearch & BM25
- PostgreSQL & MongoDB
- Kafka, Spark & Airflow
Experience
Leading AI architecture with an operator's mindset.
Translating ambitious AI ideas into secure, supportable systems—and aligning engineering decisions with enterprise outcomes.
Jun 2026 — Present
Cognizant
Senior Consulting Manager · AI Architect
Leading enterprise AI architecture for process intelligence, incident intelligence and RCA, AI observability, and multi-tenant code intelligence using context fabric, RAG, agent orchestration and cloud-native LLMOps.
Mar 2025 — May 2026
Infonox Software · Global Payments
Software Engineer Consultant
Designed bidirectional synchronization across NetSuite, Intacct, QBO and SAP, together with payment and term-discount automation, AI-powered invoice capture and developer tooling for reliable financial operations.
Apr 2015 — Mar 2025
Dell Technologies
Principal Software Engineer
Architected cloud-native data and integration platforms, developer copilots, agentic incident and patching automation, and Spark and Airflow self-service on Kubernetes—reducing MTTR by up to 60% and patch cycles from three weeks to two days.
Nov 2011 — Mar 2015
Wipro
Senior Project Engineer
Delivered enterprise integration and automation solutions for Apple and MasterCard, spanning web services, XML processing, regression frameworks and payment-oriented web platforms.
Education & qualifications
Strong foundations in mathematics, computing and applied AI.
Formal education spanning mathematics, computer applications, artificial intelligence, machine learning and advanced data science.
Master of Data Science
Deakin UniversityAustralia
Advanced data science and applied analytics
Post Graduate Program in AI & ML
The University of Texas at AustinAustin, Texas, USA
Artificial intelligence and machine learning
Post Graduate Program in AI & ML
Great Lakes Institute of ManagementChennai, Tamil Nadu, India
Applied AI and machine learning
Master of Computer Applications
SRM UniversityChennai, Tamil Nadu, India
University rank holder
B.Sc. (Hons.) Mathematics
St. Columba's CollegeHazaribagh, Jharkhand, India
Mathematics foundation for AI and data science
Professional certificationCKAD · Certified Kubernetes Application Developer
Contact
Let's make the next AI system production-ready.
Open to conversations about enterprise AI architecture, GenAI platforms, agentic systems and senior technical leadership.
ResumeDownload the complete PDF↓LinkedInProfessional profile↗Medium525+ followers · Technical articles↗Great LearningCourses, credentials and learning portfolio↗
Back to top