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Mihika Chhabra

@mihikachhabra1

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Machine Learning Engineer & Tech Lead building industrial AI for energy clients, delivering end-to-end value.

India

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What I'm looking for

I’m looking to lead end-to-end industrial AI delivery—owning roadmaps, models, and stakeholder communication—while building scalable pipelines and MLOps in agile cross-functional teams for global energy and transformation clients.

I’m an AI & Data Analytics Lead (ML Engineer / Tech Lead) at Siemens Energy, where I’ve spent 4 years building and deploying industrial AI solutions for global energy clients. I define business problems and translate them into predictive maintenance and analytics models that reduce operational downtime by 30% across enterprise accounts.

I own end-to-end service delivery—from roadmap planning and backlog ownership to C-suite communication across Europe and India. I lead cross-functional squads, drive agile delivery (Scrum/Lean), and turn complex technical capabilities into business value for gas services and energy transformation.

My work also focuses on scalable delivery: automated data handling frameworks and pipelines across AWS and Snowflake, plus production-grade ML APIs (Flask) and MLOps practices. I bring market intelligence on gas turbine services and decarbonisation to help shape high-impact product strategy and AI portfolio initiatives.

Experience

Work history, roles, and key accomplishments

Machine Learning Intern

Siemens Energy

Jan 2022 - Jun 2022 (5 months)

Conducted applied ML research across 30+ datasets for gas turbine predictive maintenance, improving fault detection accuracy by 25% and informing R&D roadmap decisions. Engineered 10+ Flask APIs with load testing, reducing inference response times by 30% for enterprise-scale deployment.

Applied Machine Learning Predictive Maintenance Fault Detection Python Flask REST APIs Load Testing Model Evaluation Gas Turbine Analytics

Research Intern

MITACS Canada

May 2021 - Sep 2021 (4 months)

Conducted user research to shape an AI feature roadmap for an elderly care application, translating findings into structured product recommendations published in an IEEE Journal. Developed a Human Activity Recognition model achieving 40% improved fall detection accuracy while collaborating with global researchers.

User Research Product Roadmapping Human Activity Recognition Fall Detection Machine Learning Deep Learning Model Evaluation Academic Publishing