Available to hire
Machine Learning Engineer with 4+ years of experience building, deploying, and scaling production-grade ML and GenAI
solutions across NLP, deep learning, and retrieval-based systems. Strong expertise in PyTorch, TensorFlow, Hugging Face, and
LangChain, with hands-on experience delivering LLM-powered applications, model monitoring, and drift detection. Proven
ability to translate business problems into scalable ML systems, leveraging AWS SageMaker, Azure ML, and Databricks in
enterprise environments.
Skills
Work Experience
Machine Learning Engineer at Kaiser Permanente
April 1, 2024 - PresentLed end-to-end ML initiatives across predictive modeling, retrieval systems, and multimodal learning, delivering production-grade solutions that supported enterprise decision-making for 5+ stakeholder groups. Designed and scaled feature engineering pipelines and standardized evaluation frameworks, delivering 18–25% improvements in precision and recall while boosting overall model reliability in production. Directed model experimentation and validation using cross-validation, A/B testing, and automated benchmarking, reducing model selection and iteration time by 30% without sacrificing performance. Established production-grade ML workflows covering training, inference, monitoring, and drift-based retraining, cutting performance degradation by 35%. Integrated ML pipelines with CI/CD automation and model versioning for repeatable deployments and faster iterations. Designed and deployed GenAI solutions with LLMs for document understanding, semantic search, and decision support. Implement
Machine Learning Engineer at Tata Consultancy Services
May 1, 2021 - June 30, 2023Built and optimized ML models for classification, anomaly detection, and recommendation on enterprise-scale datasets, improving accuracy by 20–28%. Engineered and validated feature pipelines, reducing data noise and improving model stability by 25%. Executed hyperparameter tuning and ensemble modeling strategies, lowering model variance and improving consistency across multiple production releases. Operationalized ML workflows in collaboration with DevOps and data engineering teams, cutting model deployment turnaround time by 30%. Monitored and analyzed model performance metrics post-deployment, supporting retraining strategies that reduced performance decay by 22% over time.
Jr. ML Engineer / Data Scientist at Miraicoders Technology
December 1, 2020 - July 31, 2021Supported the development and experimentation of supervised machine learning models on structured datasets, contributing to incremental accuracy improvements during model iteration and validation. Performed data cleaning, preprocessing, and exploratory data analysis (EDA) to improve dataset quality and ensure reliable inputs for model training. Assisted in evaluating model performance using standard metrics such as precision, recall, and F1-score to compare baseline and tuned models. Collaborated with senior data scientists and engineers to document model workflows, assumptions, and findings, supporting team knowledge sharing and smoother onboarding.
Education
Masters in Data Science at Texas A&M University, College Station, TX
January 11, 2030 - April 9, 2026Bachelors in Electronics and Communication Engineering at Gayatri Vidya Parishad College of Engineering, Andhra Pradesh, India
January 11, 2030 - April 9, 2026Qualifications
NVIDIA Deep Learning Institute (DLI) Certified
January 11, 2030 - April 9, 2026Databricks Certified: Generative AI Fundamentals
January 11, 2030 - April 9, 2026Python Developer Certification
January 11, 2030 - April 9, 2026Industry Experience
Healthcare, Software & Internet, Professional Services
Skills
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