Senior ML Engineer - MLOps & Generative AI Expert

REVATHI DHOTRE

SENIOR MACHINE LEARNING ENGINEER

Chicago (Open to Relocate), United States *************@*****.*** +1-312-***-**** LinkedIn PROFILE

As an innovative AI/ML Engineer with a strong foundation in high-performance computing and scalable machine learning systems, I excel in designing and deploying robust, production-ready pipelines. My expertise lies in enhancing operational efficiency and decision-making through MLOps automation, as demonstrated by a 45% reduction in decision-cycle turnaround and a 12% improvement in operational efficiency. I have a proven track record of building models over extensive datasets, such as 10M+ supercomputer job logs, to predict queue transitions and deliver interactive analytics. My skills in deep learning, generative AI, and prompt engineering enable me to optimize training and inference across GPU clusters, accelerating time-to-value. With a Master’s degree in Computer Science and hands-on experience in AI/ML model development and deployment, I am well-equipped to lead advanced AI/ML solution development and cross-functional collaboration, ensuring seamless integration and performance monitoring.

EDUCATION

Jawaharlal Nehru Technological University (JNTUH)

Bachelor of Technology in Computer Science &

Engineering

Aug 2017 — Jul 2021

University of Illinois at Chicago

Master’s degree in Computer Science

Aug 2023 — Aug 2025

CORE COMPETENCIES & TECHNICAL PROFICIENCIES

Cross-Functional

Collaboration

MLOps

Sql

Large Scale Distributed

Computing

Generative AI

Python

Containerization with

Docker and Kubernetes

Deep Learning

Cloud Data Environments

Machine Learning

Engineering

CAREER EXPERIENCE

Software Engineer I, Synopsys Sept 2021 — Aug 2023 Partnered with business process owners to optimize ETL pipelines aimed at reducing data transformation errors and aligning technical solutions with business requirements. Analyzed and documented Azure cloud service costs, while creating pricing modules to optimize infrastructure spending and support budget forecasting. Audited and validated data from multiple ingestion sources, routed through message queues, and indexed in distributed analytics platforms for critical infrastructure monitoring with aim of optimizing data pipelines.

• Reduced turnaround time and accelerated decision-making cycles by 45% across business units by enhancing 80% of manual workflows with MLOps frameworks, while collaborating with global teams.

• Enhanced data accessibility and contributed to 12% improvement in operational efficiency by designing interactive dashboards tailored to internal stakeholders.

• Architected API-driven data synchronization systems and facilitated platform migration by designing end-to-end data pipelines for seamless data synchronization and fault-tolerant systems that reduced legacy dependencies and system downtime by 50% during platform transition.

• Improved data integrity, identified/addressed bottlenecks in data pipeline performance, and enhanced system uptime through automation and infrastructure monitoring. Research Aide Tech, Argonne National Laboratory May 2024 — Aug 2024

• Achieved 15% accuracy in identifying jobs requiring computational resources by engineering machine learning models to predict debug-to-production queue transitions through analysis of 10M+ supercomputer job logs

(Polaris).

• Implemented ML infrastructures for efficient model training and deployment in supercomputing environments.

• Simplified complex supercomputer job logs into actionable insights by developing interactive data visualizations. Page 1

Research Aide Tech, Argonne National Laboratory Jun 2025 — Aug 2025 Developed web-based platform enabling interactive 3D visualization and analysis of large-scale particle- simulation datasets to grant scientists instant access to massive datasets on HPC systems. Employed Django- backend utilizing Globus Portal Framework for data transfer, coupled with Globus Compute services for- data processing on Polaris supercomputing system.

• Enhanced features in web-based ML analysis by incorporating AI/ML algorithms in Django Globus Compute workflows.

• Integrated ML and data analysis through web portals by identifying bottlenecks in production workflows- and researching parallel processing methods.

Research Assistant, University of Illinois at Chicago Oct 2023 — May 2025 Utilized High-Performance Computing (HPC) platforms like Aurora, Polaris, and Theta Supercomputers for assessing hydrological simulations and creating online data presentation tools for research purposes. Coordinated with diverse groups to roll out scalable visual analytics tools, integrated parallel system enhancements, and employed sophisticated data visualization technologies for scientific evaluations.

• Boosted system efficiency by pinpointing and rectifying bottlenecks, while enhancing uptime through proactive automation, monitoring, and performance optimization initiatives. TECHNICAL PROFICIENCIES (SKILLS)

Programming Languages: Python, C#, C, C++, Java, JavaScript Artificial Intelligence (AI): Machine Learning (ML), MLOps, AIOps - Multimodal AI, Agentic AI, AI Perception Systems High Performance Computing (HPC): OpenMP, MPI, CUDA, Parallel Processing, GPU Workflows Profiling Statistics & Data Science: Big Data Analytics, Data Visualization (Tableau, PowerBI, Looker, PyVista) Database Systems: MySQL, MSSQL, PostgreSQL, MongoDB Cloud Technologies: Amazon Web Services (AWS), Microsoft Azure, Google Cloud (GCP) Others: System Design, Linux, Data Structures & Algorithms, TensorFlow, PyTorch, GPU, Distributed Systems, Microservices

DevOps: CI/CD Pipelines, Docker, Git, Github, Kubernetes, Elasticsearch Logstash Kibana (ELK) TRAININGS

Agile Methodology PMP, Simplilearn

AI-driven science on a Supercomputer, Argonne

VOLUNTEER & COMMUNITY INVOLVEMENT

Active Community Member and Volunteer, AnitaB.org Grace Hopper Conference (GHC 2025)

Committee Member and Volunteer, CppCon The C++ Conference Active Member, Society of Women Engineer’s Affinity & Focus Groups SWE Conference 2025, 2024

Co–Teacher, Microsoft TEALS Volunteer

Technical Volunteer, TEDx Chicago

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