Sr. Staff Applied AI Engineer — Enterprise Applied AI
Rivian Automotive Inc.Orange County / Los Angeles, CA
Applied AI Engineer · Enterprise Architect · Digital Transformation Leader · AI Researcher
I lead Applied AI engineering end to end—directing the architecture, development, and production deployment of enterprise-grade AI systems. My work on global business and productivity applications transforms complex operations, improves workforce efficiency, and advances physical-asset and endpoint engineering through Applied AI.
About
The value of AI is not the demo. It is the trusted system that changes how work gets done.
Current
Rivian Automotive Inc.
Previous
Rivian Automotive Inc.
2020–2021
Samsung SDS America & Krish Service Group
2015–2019
The Goodyear Tire & Rubber Company
2008–2015
IBM · HCL Technologies · Singtel Optus · LTI
I work at the intersection of enterprise AI, business applications, and digital employee experience—turning ambiguous problems into governed systems that teams can operate, measure, and trust.
My research explores verifiable reasoning, Agentic Self-Correction, Synthetic Reasoning, Modular Program Synthesis, and human–AI collaboration. It informs how I design production systems: grounded in context, bounded by controls, and reviewed by people when decisions matter.
Education & certifications
PhD in Information Systems
Dakota State University
2025–2029 · In progress
Doctor of Business Administration (DBA) in Computer Science
IMET, USA
2023
Master of Computer Applications (MCA)
I.K. Gujral Punjab Technical University, India
2007
Bachelor of Computer Applications (BCA)
Awadhesh Pratap Singh University, India
30+ total AI & cloud certifications across architecture, cloud, agile, and Microsoft platforms.
AI & architecture
Agile & process
Microsoft
Measured outcomes
0+
Agentic AI systems architected
0
Continents delivered
0+
Cloud applications delivered
0M
Associates served globally
0M
Users on platforms architected
Full impact ledger on Recognition.
Capabilities
Practical capabilities for taking AI from strategy and architecture through production, governance, and adoption.
Multi-agent systems with self-correction, tool use, and verifiable reasoning — Bedrock, Vertex AI, LangChain, from paper to production.
Multi-cloud transformation, Terraform, Kubernetes, CI/CD — AWS, Azure, GCP at Rivian, Samsung, and Fortune 100 scale.
EV ecosystem AI, computer vision defect detection, digital twins, and intelligent automation for next-generation programs.
Synthetic reasoning, modular program synthesis, RAG, and faithful computation beyond post-hoc rationalization.
IEEE Senior Member; ISO 42001, NIST RMF, SAIL lifecycle; published on ethical AI in EV and humanitarian contexts.
Design Thinking, global delivery, platform engineering, and stakeholder alignment across Asia, North America, Europe, and Australia.
Technical footprint
Research & scholarship
Featured publications spanning agentic systems, verifiable reasoning, retrieval, and software-defined vehicles.
N Shukla, K Pandey, O Tiwari · arXiv:2607.16973 · 2026
Introduces TurboVec, an open-source index built on TurboQuant — a codebook-oblivious scalar quantizer that needs no corpus-dependent training. On DBpedia embeddings, 4-bit TurboQuant beats trained FAISS Product Quantization on Recall@5 and cuts query latency dramatically, while the codebook-oblivious design lowers membership-inference risk to near-random — cost-efficient, privacy-preserving retrieval for enterprise RAG.
2026 2nd Int. Conf. on Emerging and Intelligent Technologies and Systems (EITS) · IEEE · Jun 2026
Examines how quantization can induce robustness collapse in edge-deployed language models, and proposes security and governance frameworks for 6G IoT ecosystems.
2026 2nd Int. Conf. on Artificial Intelligence Systems (AIS) · IEEE · May 2026
Presents a framework for next-generation predictive maintenance of software-defined vehicles.
Recognition
Individual honors presented with their issuing organization and date.
| Honor | Issuer | Date |
|---|---|---|
| Artificial Intelligence 150 (AI150) — 2025–2026 | Constellation Research Inc. | 2025 |
| Artificial Intelligence 150 (AI150) — 2026–2027 | Constellation Research Inc. | 2026 |
| BoxWorks Customer Award — Ecosystem Innovator | Box, Inc. | Sep 11, 2025 |
| Best Performing Project Award | L&T Infotech | Sep 19, 2014 |
| Valuable Contribution Award | L&T Infotech | Undated |
| Top 100 ADPList Mentor | ADPList | Oct–Dec 2024 |
| R&D Innovation Award | HCL Technologies | 2011 |
Professional service & leadership
Team recognition: L&T Infotech Team Performance Award — HBO SharePoint Team. Presented as team context, not as an individual honor.
Production AI & transformation
From architecture to adoption, I build systems that move real work.
My portfolio connects agentic AI, business applications, manufacturing intelligence, computer vision, quality, supply chain, and software-defined vehicles. Each platform is designed for production reliability, measurable value, and responsible human oversight.
The goal is not autonomous output. It is trusted intelligence that improves decisions, accelerates delivery, and transforms operations.
Context-first
Not prompt-first — assemble the business and technical context the work requires.
Work products
Architecture packages, investigation summaries, decision records — not chat demos.
Governance in-product
Approvals, audit trails, versioning, and validation gates are first-class.
Trust as engineering
Completeness, faithfulness, freshness, and precision as design criteria.
Flagship · Agentic intelligence layer
AI-powered software delivery with governance built in
Problem
Meaning fractures between requirement and implementation — designs drift, reviews arrive late, and teams duplicate work.
Solution
A coordinated multi-agent lifecycle that turns requirements into architecture, design, validation, and delivery-ready artifacts with approvals, version history, and end-to-end traceability.
My contribution
Shaped platform architecture, owned key intelligence and validation capabilities, contributed to gate-control logic, and defined how architecture decisions become traceable implementation work.
Design target
8-day
Target PRD → engineering cycle (14-day max)
Turning fragmented case intelligence into action
Problem
Investigators and legal teams assemble a complete picture from many systems before they can act — slow, repetitive, hard to scale, and inconsistent.
Solution
A governed retrieval and synthesis platform for citation-backed summaries, report generation, vehicle/case-scoped evidence, audit trails, and human confirmation before consequential updates.
My contribution
Shaped and stress-tested the architecture; established secure retrieval, AI analysis patterns, and human-reviewed workflow automation for high-stakes product-integrity work.
Design target
<5 min
Target VIN research (from 2–4 hours)
The foundation for scalable, governable AI agents
Problem
One useful assistant is easy; a family of reliable agents is hard — lost context, stale answers, unbounded loops, and inconsistent behavior across environments.
Solution
A control plane for agentic work: event-driven orchestration, explicit handoffs, bounded validation loops, versioned outputs, concurrency controls, and operational observability.
My contribution
Contributed to platform architecture, agent design, deployment coordination, access and infrastructure planning, and the control patterns that make agentic systems governable at enterprise scale.
Design target
5 agents
Target coordinated stages · ≤3 validation cycles / gate
Publishing note. KPI and ROI figures above are design targets or modeled value opportunities from platform criteria — presented as targets until validated with measured production results. Career timeline and prior roles: Experience.