Professional Summary
I focus on Data, ML, and Go-To-Market to realize cloud-optimized architectures. My areas of expertise include ML solution architectures, AWS cloud architectures, language model customization, AI Research, ML Infrastructure, distributed computing, MLOps, application development, program management, business intelligence, system integrations, and automations. I also lead enterprise AI transformations, driving strategy development, execution, and delivery to achieve scalable, business-aligned outcomes. I oversee multi-country initiatives, leading teams across solution architecture, sales, business development, and AI services/products to accelerate AI adoption.
Career Interests
- Applied ML
- Data Centric AI
- Internet of Things (IoT)
- Business Analytics & Strategy
Current Job Responsibilities
- Work with customer's data science team to deeply understand their business and technical needs. Lead their AI/ML team to design solutions that make the best use of the AWS cloud platform and AI/ML Services.
- Partner with solution architects, sales, business development and the AI service teams to accelerate customer adoption and revenue attainment.
- Lead the AWS response to data and AI/ML transformation opportunities and scale partner solutions across multiple customers.
- Act as a technical liaison between customers and the service engineering teams and provide improvement feedback.
- Develop and support an AWS internal community of AI related subject matter experts
- In partnership with the sales team, formulate and execute a sales strategy to exceed business unit revenue objectives through the adoption of AWS. Drive large, complex sales opportunities to closure and through delivery
- As a key member of the business development and sales team, ensure success in building and migrating applications, software and services on the AWS platform.
- Educate customers on the value proposition of AWS, and participate in deep architectural discussions to ensure solutions are designed for successful deployment in the cloud. Ensure customer success in ideating and launching AI/ML on the AWS platform
- Conduct workshop sessions to identify AI/ML opportunities with customers considering or already using AWS
- Build deep relationships with senior technical individuals and executive leadership within customers to enable them to be cloud advocates
- Author AWS customer-facing publications such as whitepapers and contribute to the creation of best practices, packaged offering, blogs, workshops, etc.
Past Projects - 2017 to 2019
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Work order demand prediction for capacity management using time series & bayesian modeling (Field Service)
Forecasted the demand of incoming work orders from customers to optimally allign supply of technicans at different levels of granualarity using various time series methods. -
Device level proactive network outage prediction using classification methods (Network Infrastructure)
Used classification techniques to predict device failure for proactive resolution of customer installs. -
Network health monitoring using anomaly detection methods (Network Monitoring)
Used anomaly detection techniques to monitor true health state of network & customer premisis equipment that allows proactive measures to be taken ensuring customer satisfication. -
Developed models to predict and prevent avoidable truck rolls using classification methods (Customer Support)
Used network telemetry and topology infomation to predict type of truck roll using tree based classifiers while accounting for extreme class imbalance. -
Developed recommendation & prediction models for transaction reduction within the context of employee digital workplace services (IT Helpdesk)
Collated data from disparate data sources and mined for deeper insights & identified opportunites for transaction reduction. Developed and validated use cases, used text mining for helpdesk service ticket categorization & developed recommendation models for next best action -
Regression modeling of aircraft engine health for predictive maintenance.(Aerospace)
Modeled turbine engine performance using operational parameters, demonstrated engine deterioration, measured engine's state & health index to predict maintenance costs. -
Modeled aircraft operator use/abuse profile using clustering methods (Aerospace)
Using feature engineered valiables, applied clustering methods to develop models that classify operator behavior using disparate data sources and developed optimization models for pricing of maintenance service plans and extended warranty support -
Developed predictive models for turbine engine subsystems using pattern mining methods (Aerospace)
Developed pattern mining models for engine subsystems that reflect component wear. Developed diagnostics and prognostics algorithms using data driven approach validated by physics based models.
Overall Summary
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Consulting
- Collaborated with global teams to design and implement scalable AI/ML solutions across industries, leveraging client data to solve business problems.
- Engaged with CxOs to communicate the value of data science, service offerings, and guide implementation.
- Gathered client requirements, managed teams, and presented findings to executives.
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Architectural Design and Development
- Led end-to-end machine learning projects, covering planning, design, technical implementation, and MLOps.
- Defined solution architectures and translated executive needs into successful data science implementations.
- Delivered data-driven recommendations and presented complex technical concepts clearly to clients.
- Analyzed and modeled structured and unstructured data, conducting exploratory data analysis and feature engineering to improve model performance.
- Managed internal and external data analysis to drive decision-making, including database queries and statistical analysis.
- Assisted in customer engagement, project scoping, timeline management, and results documentation.
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Thought Leadership
- Participated in conferences, summits, and meetups to share insights with internal teams and external audiences.
- Contributed to thought leadership by managing client relations within an existing and potential client network.
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Trusted Advisor
- Provided guidance on multiple engagements to ensure successful delivery while balancing internal initiatives
- Helped craft the long-term data strategy for the clients and advocated for the intelligence use of data throughout the business
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Practice Development
- Developed analytics sub-practice within telecom sector. Generated and executed the Data Science roadmap strategy
- Assisted in growing the data science practice by meeting business goals through client prospecting, responding to proposals, identifying and closing opportunities within identified client accounts.