Amazon Web Services (AWS)

• Lead global AI transformation initiatives for enterprise customers, shaping strategy, architecture, and operating models for deploying agentic AI systems at scale.
• Influence adoption across multiple regions by working with executive leadership to define AI roadmaps, governance models, and organizational structures for AI-native transformation.
• Design and promote scalable AI deployment patterns, including context engineering frameworks and multi-agent architectures, enabling repeatable adoption across diverse enterprise environments.
• Drive cross-functional alignment between customers, product, and engineering teams, influencing product direction based on real-world deployment needs
• Lead multi-region initiatives, coordinating globally distributed teams across solution architecture, engineering, and go-to-market functions. Establish best practices and internal mechanisms to scale AI expertise across the organization

Specialist in bridging systems engineering rigor with foundation model orchestration, data pipelines, context engineering, agentic AI design, and enterprise ML infrastructure - spanning LLMOps, AgentOps, and RAGOps at production scale.

Amazon Web Services (AWS)

• Partnered with customers to translate complex business challenges into deployable AI systems, from ideation through production
• Architected and led development of AI solutions, enabling customers to operationalize AI/ML at scale
• Designed reusable architectures, workshops, and field enablement programs to scale adoption across multiple organizations
• Acted as a trusted advisor to senior stakeholders, shaping AI strategy, adoption roadmaps, and organizational alignment
• Authored thought leadership including research papers, blogs, and conference materials, influencing global AI adoption

Specialist in distributed computing, SDLC, system integration, and automations.

Amazon Web Services (AWS)

• Led co-development initiatives with partners and customers to modernize AI/ML systems
• Built trust and technical credibility, accelerating adoption of cloud-based AI platforms
• Delivered architecture reviews, POCs, and production guidance for complex AI deployments

Specialist in cloud integrations, lift and shift, cloud native ML developments, cloud automation, and MLOps.

Capgemini

• Designed and operationalized AI delivery models across multiple clients, enabling repeatable deployment of machine learning systems across industries.
• Led a globally distributed team of 30+ engineers to design and deploy AI solutions at scale across industries
• Partnered with executive leadership to shape AI strategy, data governance, and enterprise transformation initiatives
• Delivered large-scale AI programs from concept through production, integrating ML into operational workflows
• Played a key role in business growth through client engagement, solution design, and opportunity development

Clients: Cox Communications, Rogers Communications. Specialist in supervised learning, pattern mining, anomaly detection, time-series, optimization, and deep learning. Industry domain expertise in telecom.

Cyient Insights Inc.

• Architected end-to-end predictive systems for aerospace applications using distributed data pipelines
• Led a team of 10+ data scientists to develop solutions across multiple industries – Aerospace, Defense, Heavy Engineering
• Designed scalable ML pipelines and operationalized AI systems aligned with engineering constraints
• Translated executive objectives into production-grade machine learning architecture
• Designed data workflows, engineered pipeline orchestration, feature engineering processes, and automated retraining loops
• Applied systems engineering principles to align ML outputs with operational constraints
• Collaborated cross-functionally with domain engineers and leadership to operationalize AI capabilities.

Client: Honeywell Aerospace Technologies. Specialist in predictive analytics and building machine learning pipelines for the aerospace industry. Define solution architectures using engineering knowledge and translate executive requests into successful data science implementations. Industry domain expertise in aerospace and manufacturing.

RTX

• Engineered safety-critical aerospace systems with a focus on reliability, resilience, and failure mitigation
• Led component- and system-level simulations across structures, dynamics, impact and reliability domains
• Conducted root cause analysis and designed systems for deterministic and probabilistic performance
• Developed deep expertise in system safety, risk modeling, and operational robustness, foundational to responsible AI thinking
• Designed for deterministic behavior in tightly coupled hardware–software control environments
• Embedded disciplined systems engineering practices into every stage of design and validation.
• Embedded disciplined systems engineering practices into every stage of design and validation

Specialist in design, stress analysis, thermal analysis, dynamic analysis, impact analysis, fracture mechanics, fatigue life evaluation, MRB activities, reliability engineering, structural testing, and root cause investigations.

Software Techniques Inc.

• Developed custom MATLAB simulation software to model nonlinear drill string vibration dynamics.
• Designed scalable computational frameworks implementing physical models and numerical solvers for complex, multi-parameter systems.
• Abstracted finite element complexity into modular, maintainable software architecture to improve usability and extensibility.

Client: Weatherford. Industry domain expertise in Energy (Oil & Gas).