01 — GENAI
Enterprise GenAI & RAG
Knowledge-grounded applications with a focus on retrieval relevance, context quality, model integration, and useful responses.
AI engineering · Architecture · Delivery
Turning AI potential into production systems.
I’m Avinash Kumar. I lead the development of enterprise GenAI, agentic AI, and ML/DL systems—from prototype to production.
Enterprise AI · RAG & LLM applications · Agent platforms · Cloud-native delivery
AIBuilt to work
Context & retrievalGrounded intelligence
Agentic workflowsTools · state · orchestration
Production systemsDeploy · observe · improve
Platform thinkingReusable by design
13+ yearsBuilding AI/ML systems across finance, mobility & enterprise
Enterprise AIGenAI systems built around real workflows
End-to-endArchitecture through deployment and operations
Global deliveryLeading and mentoring AI engineers across countries
01 / Profile
Engineering beyond the demo.
The goal isn’t simply to make a model answer. It’s to build a dependable system around it — one that can be integrated, evaluated, deployed, and improved.
My work sits at the intersection of applied AI, software architecture, and cloud-native engineering. I’ve worked across enterprise AI, machine learning, intelligent document processing, computer vision, and mobility analytics.
In GenAI, I focus on the full application path: preparing knowledge, retrieving and reranking context, integrating foundation models, orchestrating tools, and designing the operational foundations that make the solution maintainable.
I’m especially interested in reusable AI platforms — systems that make it easier to build, govern, deploy, and operate agents without reinventing the same foundations for every use case.
Solution architectureApplied MLRAG & retrievalAgentic AIDeveloper platformsCloud-native systemsAI governanceAgent lifecycle managementLLMOps & observability
02 / Core expertise
From intelligence
to implementation.
A practical blend of AI depth and production engineering — designed to solve business problems, not just showcase technology.
02 — AGENTS
Agentic systems
Tool-enabled workflows, agent catalogues, structured interfaces, orchestration, and the right balance between autonomy and control.
03 — PLATFORMS
AI platform engineering
Reusable foundations for creating, packaging, deploying, and observing AI-powered applications and custom agents.
04 — ML
Applied ML & data
Machine-learning workflows across structured data, text embeddings, document processing, and computer vision use cases.
05 — ARCHITECTURE
System design & APIs
Modular services, clear contracts, integration patterns, and architecture choices grounded in operational trade-offs.
06 — DELIVERY
Cloud-native delivery
Containerized workloads, infrastructure automation, deployment troubleshooting, and visibility into runtime behavior.
07 — GOVERNANCE
AI governance & responsible AI
Governance-minded AI delivery with attention to risk controls, evaluation, traceability, access boundaries, and responsible deployment practices.
08 — LIFECYCLE
Agent lifecycle management
Managing agents across design, testing, versioning, deployment, monitoring, and continuous improvement — with clear ownership and operational controls.
03 / How I work
Design the whole system.
Strong AI solutions require more than model selection. They need a clear path from requirements to measurable, supportable software.
Architecture principle / 01
Make complexity manageable.
Break the solution into explicit stages, interfaces, and responsibilities. Reuse existing capabilities where they fit; add autonomy only when it creates value.
- Modular components and standard contracts
- Explicit assumptions, dependencies, and failure paths
- Separation of UI responsiveness from backend execution
Architecture principle / 02
Build for life after launch.
Plan for the operational questions early: how the system is deployed, observed, evaluated, secured, and improved over time.
- Deployment automation and infrastructure as code
- Logs, status, and actionable diagnostics
- Retrieval and model behavior evaluated in context
From intent to governed execution
An agentic workflow, with RAG when needed.
Plan and execute tasks through tools and specialized agents. Ground decisions in enterprise knowledge when retrieval is relevant, with evaluation and governance across the lifecycle.
Understand intent→Plan task→Select agents & tools→Execute & validate→Evaluate & improve
Optional RAG capability→Retrieve→Rerank→Ground response
Governance layer: permissions · traceability · evaluation · lifecycle monitoring
04 / Selected work
Experience across domains.
Platforms and AI systems I’ve led or built — from agentic GenAI at IBM to computer vision for railways and autonomous driving.
IBM · Agentic AI
Agentrix
Led development of an autonomous platform that turns high-level requirements into deployable AI agent solutions — clarifying intent, designing workflows, generating tool specifications, and producing ready-to-use implementations.
IBM · watsonx
Enterprise GenAI solutions
Production GenAI for multiple enterprise clients: GenAI-driven code development lifecycle, an enterprise GenAI coach, RAG and Text-to-SQL, LLM fine-tuning, and compliance, governance, and evaluation frameworks.
Hitachi Vantara · Mobility
Railway network monitoring
Vision-based analytics on image and video data — scenario coverage, anomaly detection, and quality analysis — deployed on daily operations and covering 90,000+ km of railway runs.
Altran · Automotive
AutoM — autonomous driving
Architected a scalable, low-latency prediction pipeline on AWS and vision models for object detection and scene understanding, with Trusted AI: explainability, fairness, robustness, and drift monitoring.
Rage Frameworks · Finance
CORA AI — document intelligence
Built a hybrid BERT model for text association and segment mapping of financial documents, plus an OCR pipeline combining CNNs, filters, Tesseract, and OpenCV to build knowledge graphs.
05 / Experience
Professional experience.
Over thirteen years of building software and AI systems across enterprise, finance, mobility, and automotive domains.
Nov 2023 – Present
Advisory AI Engineer
IBM · Bengaluru
Leading Agentrix and production GenAI delivery on watsonx for enterprise clients; building and mentoring a team of AI engineers delivering across multiple countries.
Dec 2021 – Nov 2023
Senior Consultant
Hitachi Vantara · Bengaluru
Vision analytics for rail networks, a rule-based deduction engine for bulk satellite data, and productionized time-series forecasting for inventory demand planning.
Jun 2020 – Nov 2021
Senior Consultant
Altran · Gurugram
Autonomous-driving AI framework, Trusted AI for safety-critical systems, and computer-vision solutions for cooking automation and transformer fault detection.
Dec 2018 – Jun 2020
Consultant
Rage Frameworks · Gurugram
Transformer-based NLP and OCR for knowledge-graph generation from financial documents.
Dec 2012 – Dec 2018
IT Analyst
Tata Consultancy Services · Gurugram
Customer-facing financial platforms deployed across EMEA, APAC, and LATAM; cross-sell prediction models; guided teams of 6–8 engineers.
06 / Technology
The tools behind the work.
A cross-functional toolkit spanning AI, application engineering, data, orchestration, and infrastructure.
AI & machine learning
Watsonx.aiWatsonx OrchestrateLangChainLangGraphLangSmithLangFlowCrewAIAgent tool integrationStructured tool schemasTensorFlowKerasScikit-learnOpenCVsentence-transformersMilvus
Engineering & infrastructure
PythonJavaSQLPySparkFlaskREST / SOAPDockerPodmanKubernetesOpenShiftTerraformAWSAzureIBM Code EngineGitHub
07 / Start a conversation
Have a complex AI problem to solve?
I’m interested in conversations about enterprise GenAI, agent platforms, AI architecture, and turning promising prototypes into robust systems. If you’re building in this space, let’s compare notes.