• Leading the design and development of Vessel.ai – Ford’s enterprise-scale AI powered engineering copilot valued currently at $50M, built to accelerate vehicle development through GenAI-driven engineering and cross-feature intelligence.
• Conceived, architected, and now leading the development of an agentic AI system that unifies data from 400+ vehicle features and 50 different program lines – including feature documents, requirements, vehicle architectures, ECU architectures, signals and wiring diagrams, functional safety artifacts, and test data behind a secure, natural- language interface.
• Developing prototypes for specialized structured and unstructured agents using LangChain, LLM orchestration frameworks, and custom retriever pipelines to build and evaluate unique engineering use cases across product development domains. Designed agents capable of reasoning over both structured and unstructured data, enabling intelligent querying, cross-feature conflict detection, and context-driven recommendations for engineers.
• Built and validated the business case for Vessel.ai by demonstrating its ability to predict integration conflicts, detect design mismatches, and accelerate diagnostics and validation. The resulting enterprise impact elevated the program to CEO-level visibility as one of Ford’s strategic AI Big Bets.
• Defined end-to-end product strategy and roadmap — translating high-impact engineering pain points into sequenced milestones, success metrics, and cross-domain AI integration goals spanning global vehicle programs.
• Architected and lead development of technical design: multi-agent stack (unstructured RAG agent, structured retriever agent, orchestrator agent), a GCS based data lake, BigQuery schemas, ETL pipelines to pull data from various source systems, vectorization pipelines to build text embeddings, an elastic-search vector database, and a front-end built on React.js.
• Orchestrating cross-functional execution across AI AC, IT, UI UX, and various core engineering teams.
  • Leading data lake development with the IT team: inventoried all source systems, co-designed data extraction & ETL pipelines, designed BigQuery schemas and landed requirements, test-cases, ECU parameters, and spec tables. Defined data ontology and column-level semantics so the structured-query agent can automatically select, join, and filter data on demand while enforcing data-governance and SSO guard-rails.
  • Developing an agentic stack and backend with an AI advancements team: developing data vectorization pipelines; designing, testing and validating structured and unstructured agents; leading development of the orchestration layer; translating high-impact engineering use-cases into computational logic, and conducting prompt fine-tuning for various business and engineering use cases.
  • Working with multiple global engineering teams in Australia, India, Germany, and Brazil to inventory their AI/RAG pilots, map overlaps, and craft a unified multi-tool integration roadmap for vessel.ai. Conducting deep architecture and code reviews, aligning priorities, negotiating shared milestones, and channeling each team’s domain intelligence into one enterprise-scale solution that meets their business needs while accelerating our own product strategy.
  • Orchestrating a 100-engineer, six-country coalition – running weekly syncs to align feature-team roadmaps, integration milestones, and enterprise AI strategy. Tracking progress against OKRs and surface risks/next actions for review and realignment.
• Launched an early-access program with 100 engineers across US, EU, APAC, and LATAM integrating full OAuth + FIM authentication and conducting live testing to improve retrieval quality, contextual reasoning, and citation accuracy.
• Leading a team of three product designers to plan and execute user interviews, usability studies, and design iterations. Converting research findings into UX improvements, new feature concepts, and refined interaction flows to optimize engagement and productivity across global engineering teams.

Systems Engineering

• Utilized MagicDraw to build SysML models, developing logical and physical architectures for Drive-by-Wire (DBW) systems, including steer-by-wire, throttle-by-wire, brake-by-wire, and shift-by-wire.
• Captured and integrated different OEM vehicle architectures into SysML models to guide software and hardware development, and seamless integration and tracking.
• Modularized DBW architecture development to establish a centralized single source of truth for various customer projects.
• Engineered and established traceability between hardware requirements, software requirements, logical and physical architectures, FMEAs, FTAs, safety goals, Functional Safety Requirements (FSRs), Technical Safety Requirements (TSRs), and test cases to maintain a digitally traceable chain for design validation.
• Leveraged logical architectures for software development and physical architectures for hardware development, performing integration at subsystem, platform, and vehicle levels.

Safety Engineering

• Used APIS-IQ software to develop Failure Modes and Effects Analyses (FMEAs) and conducted Fault Tree Analyses (FTAs) with MagicDraw for safety-critical applications in DBW integrated OEM vehicles.
• Established safety goals, functional safety requirements (FSRs), and technical safety requirements (TSRs) in compliance with ISO 26262 and ISO/PAS 21448 standards.
• Maintained traceability between safety goals, FSRs, and TSRs, reconciling FMEA and FTA findings to develop and implement necessary changes to software and hardware architectures.
• Developed technical safety concepts and AIAG FMEA Handbook compliant MSR analyses to ensure rigorous safety measures were in place.
• Created safety recommendations based on analyses and implemented safety mechanisms, addressing any design deficiencies identified during safety analysis.

Software Development and Testing

• Utilized a ROS2 framework in an Ubuntu environment to aid platform development using C++.
• Wrote and injected fault scenarios using C++ for fault injection testing, strategically placed them within the code for activation during testing.
• Employed the LDRA tool suite for comprehensive code reviews, ensuring code compliance with industry standards.
• Performed static analysis, unit testing, dynamic analysis, system integration testing, and load & stress testing.
• Addressed cyclomatic complexity, coupling, and cohesion in code to optimize performance and maintainability.
• Developed test cases based on Software, Hardware, Functional Safety (FSRs) and Technical Safety Requirements (TSRs), ensuring traceability and alignment with design specifications.
• Verified the functionality of implemented safety mechanisms and addressed design deficiencies identified during safety analysis.

Project Management

• Managed five multi-million-dollar contracts, interfacing with internal and external stakeholders to translate client needs into actionable system requirements.
• Conducted weekly calls with clients to provide updates, insights, and address concerns, ensuring clear communication and progress tracking.
• Handled quotes, purchase orders (POs), invoicing, resource allocation, scheduling, risk management, project documentation, tracking budgets, and managing time spent by different engineering teams.
• Worked with external contractors, issuing POs, tracking their work progress, and ensuring timelines and milestones were met.
• Served as the interface between external contractors and end clients, maintaining synchronization of timelines and progress across all project levels.

• Interfaced with Ford’s software, vehicle platform systems, manufacturing plant facilities, production IT and cloud teams along with external vendors to design and develop the Autonomous Vehicle Marshalling platform.
• Developed associated logical and technology architectures using SysML modeling elements.
• Conducted cross-functional stakeholder meetings with partner teams to understand current capabilities, resource availability, and identify core areas of development.
• Broke down primary high-level feature functionalities to elicit software, hardware, functional safety, and cybersecurity requirements at the concept, stakeholder, logical, and technology abstraction levels.
• Conducted requirement analysis across multiple levels of abstraction to identify and address gaps to streamline the requirements engineering process and established an end-to-end traceability.
• Led the development of technology functions and established behavioral parameters to conduct thorough FMA and subsequent FMEA analyses.
• Deployed Jama as a requirement engineering solution to help develop, review, and maintain requirements.
• Explored various systems engineering tools, methods, and processes for deployment and integration into existing workflows and toolchains.

Conducted research on driver assist features and centralized architectural frameworks to develop ADAS/Autonomy systems that would be deployed on next gen production ready vehicle platforms.

• Led R&A’s systems engineering research effort on ADAS trailering feature platforms such as PTFG and PTHA.
• Engineered requirements at the stakeholder, concept, logical, and technology levels.
• Developed logical and technology architecture models by building state machine diagrams, boundary diagrams, behavioral models, and functional flow models using SysML.
• Worked with cross-functional production teams on realizing, testing, verifying, and validating system designs and models by allocating hardware choices to an implementation-agnostic technology design.
• Designed scenarios and test cases to ensure broader coverage of system tests.
• Conducted functional safety analysis and updated the safety case framework on a cyclical basis as the feature evolved; investigated hazards and analyzed risks for ADAS features under development.
• Developed a centralized ADAS/Autonomy reference architecture model to be used as a single source of truth for various system functions and ADAS features.
• Used Syndeia Intercax to develop an integrated digital thread and establish end-to-end bi-directional traceability between requirements, system models, and test artifacts.
• Used Ansys ModelCenter for concept and architectural space exploration, behavioral simulations, trade-off analysis, identifying design deficiencies, and determining operational ranges for performance requirements.
• Deployed variant management protocols using Product Line Engineering to streamline control, inheritance, and tracking of multiple generations of features within the same architectural models.
• Explored various systems engineering tools, methods, and processes for deployment and integration into existing workflows and toolchains.

• Worked as the functional owner of various autonomy capabilities and features to develop system requirements and conduct safety reviews with relevant stakeholders.
• Developed and leveraged various CONOPS, Functional Flow Diagrams, Activity Diagrams, State Machine Diagrams, and Data Flow Diagrams to map logical and physical architecture and derive performance requirements and associated parametric data.
• Conducted System Design Analysis to identify gaps and resolve conflicts in requirements.
• Used SysML to develop operational, functional, performance, and behavioral models for validation and regression testing of ISO26262 compliant safety-related and mission-critical applications.
• Developed SysML models to capture hardware and software architecture at the logical and physical levels.
• Developed and assessed subsystem level FMEAs and FTAs using Ansys Medini.
• Developed and deployed an organization-wide Operational Domain Taxonomy-based V&V Test framework to establish strong end-to-end traceability between requirements, test artifacts, and results.
• Designed, developed, and deployed workflows and test artifacts on the Jira-Xray platform.
• Collaborated with ITENG teams to develop and deploy a complex integrated toolchain consisting of Jama, Jira-XRAY, GitHub, and other in-house tools to optimize test artifact generation, streamline testing workflows, and establish bidirectional traceability with requirements.
• Worked with various test teams to develop and review test cases, test plans, and test metrics for autonomy features at the system and subsystem levels.
• Designed and deployed a complex System of Systems framework to engineer and house system and subsystem-level functional, performance, interface, and safety requirements for various autonomy capabilities.
• Developed, deployed, administered, and utilized various projects, workflows, and item types to support these processes.

• Improved a prototype model with trade-off analysis to obtain MVP reconciling various stakeholder requirements.
• Decreased prototyping time by 50%, reducing errors with the creation of interface matrices, decision matrices, and SysML models to establish traceability between various knowledge bases due to the lack of SMEs.
• Eliminated the need for multiple iterations with concept sketches, use case behavioral and activity diagrams, CONOPS, FFBDs and IDEFOs to establish a framework.
• Reduced probability of failure causing a lifetime increase of 4 years, reduced R&D costs by $15K, and an ability to undercut competitor retail prices by $30 per unit by using DFMEAs, FTAs, and six sigma principles.
• Identified, analyzed and mitigated hazards to increase operational safety of product by developing test cases for behavioral and non-behavioral requirements of hardware and software components; used verification cross reference matrices to track requirements’ verification.

• Closed a $205K contract by gathering customer requirements, eliciting system and subsystem level functional & performance requirements, conducting feasibility analyses, and outlining total project estimates to define a custom solution. Administered this budget.
• Reduced prototype development timelines by utilizing SME’s to oversee electronics, manufacturing, and QC, balancing workloads for focus on system design, prototyping, material procurement, testing, and QA.
• Finished prototyping in under 1-month by using Systems engineering methodologies such as CONOPS, Behavioral Diagrams, Activity Diagrams, FFBDs, IDEF0s, Behavioral and Non-behavioral test procedures, and requirements verification.
• Cut material acquisition costs by $3K through a vendor exclusivity clause for upcoming projects. Saved $2K on 3rd party manufacturing costs by supplying procured material versus using vendor materials.
• Determined probability of product failure, utilizing behavioral and non-behavioral requirements for testing to include FTAs, FMEAs, and HARAs. Obtained an external 3rd party QA assessment.

• Secured INR 3.5M ($50K) in funding and INR 500K ($7K) in sponsorship money to design and build an integrated swarm system of an autonomous surface vehicle, autonomous underwater vehicle, and an unmanned aerial vehicle.
• Generated proof of concept in less than 3 months using model-based systems engineering methodologies, concept sketches, use case diagrams, FFBDs, and IDEFOs to reduce R&D and development time.
• Built the architectural framework with navigation, computer vision, and communication protocols on C++, MATLAB, using sensor data from cameras, GPS, IMU, Lidars, and hydrophones to write the programming.
• Completed 1-month ahead of schedule, allowing for additional SIL and HIL testing on data acquisition programs and system-level testing with sensors after integration of software components.
• Increased viability with a sustainable CV program to work in various light conditions through an FMA on the different situations that could obscure vision and developed filtering and enhanced protocols.

• Improved strap-down box strength without sacrificing payload space, optimizing overall structural performance.
• Added precision-engineered ribs and gussets—validated across multiple material, shape, and size trials—to deliver inherent reinforcement and vibration damping for the internal electronics.

Course Work:

• High Performance Embedded Programming
• GPU Accelerated Computing
• Embedded AI
• Edge AI
• Adaptive Control Systems
• Fault Tolerant Systems
• Intelligent Control Systems
• Large Scale Dynamic Systems
• Model Predictive Control
• Mathematical Analysis
• Adaptive Control Systems
• Estimation and Control Theory

Course Work:

• Software Modeling
• Software Verification and Testing
• Visual Computing
• Advanced Algorithms
• Computer Architecture
• Embedded Systems
• Autonomous Vehicle Systems

Course Work:

• Model Based Systems Engineering
• System Analysis, Behavior and Optimization
• Design and Operation of Reliable Systems
• Systems Architecture
• System Modeling
• Design Thinking for Complex Systems
• Project Management

Course Work:

• Electrical Machines
• Electrical Circuits
• Materials Technology
• Thermodynamics
• Fluid Power Systems and Control
• Instrumentation Engineering
• Linear Integrated Circuits
• Digital Electronics
• Controls Engineering
• Probability and Statistics
• Sensors and Actuators
• Micro-Electro Mechanical Systems
• Digital Image Processing
• Microprocessors and Microcontrollers
• Programmable Logic Circuits
• Advanced Control Systems
• Automotive Electronics
• Digital Logic Circuits
• Design of Mechatronic Systems
• Power Electronics
• Industrial Electronics