Amazon Web Services (AWS)

• Partner with customers to design and build resilient Gen AI and Agentic AI architectures.
• Advise on strategies for building, migrating, and scaling AI workloads in the cloud.
• Design industry solutions involving large-scale data processing and modeling.
• Help shape and execute AI strategies to build deep adoption and broad use of AWS.
• Accelerate enterprise Agentic AI adoption via context engineering principles.

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)

• Understand business challenges and facilitate ML ecosystem implementation.
• Engage with line of business to ideate and demonstrate solutions.
• Architect solutions, build POCs and MVPs, and establish a clear path to production.
• Author AWS blogs, whitepapers, workshops, and demos for major conferences.
• Develop market penetration strategies to enable broad use of AWS ML.

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

Amazon Web Services (AWS)

• Co-developed with partners in building and modernizing their AI/ML solutions on AWS.
• Built mind share, established partner trust and nurtured adoption of AWS ML services.
• Accelerated partner co-build and GTM through design reviews, POCs, and technical resolutions.

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

Capgemini

• Designed and implemented enterprise-scale data science and ML architectures for telecom objectives.
• Translated complex business requirements into structured machine learning problem frameworks.
• Led ML programs from planning and modeling through deployment, testing, and operational rollout.
• Built and deployed scalable ML systems leveraging structured and unstructured distributed data sources.
• Performed advanced exploratory analysis and feature engineering to improve model performance.
• Delivered executive-level presentations articulating AI strategy, model insights, and business impact.
• Partnered with product and engineering teams to integrate analytics into operational workflows.
• Advised client leadership, including CxOs, on AI adoption strategy, data governance, and data roadmaps.
• Mentored junior data scientists and contributed to scaling the broader data science practice.
• Supported business development through proposal responses and solution designs.

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 analytics systems for aerospace applications.
• Designed scalable ML pipelines from data ingestion through model deployment.
• Translated high-level executive objectives into production-grade machine learning architectures.
• Designed data workflows integrating engineering telemetry and structured operational datasets.
• 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 for structural integrity and failure containment.
• Led component- and system-level simulations across structures, dynamics, impact, and reliability domains.
• Analyzed and mitigated failure modes through root-cause analysis and resilience strategies.
• Conducted root cause investigations and damage tolerance assessments to prevent catastrophic failure.
• Designed for deterministic behavior in tightly coupled hardware-software control environments.
• Embedded disciplined systems engineering practices into every stage of design and validation.
• Balanced deterministic control systems with probabilistic modeling approaches.

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).