Payal Kosbatwar

About Payal Kosbatwar

I’m an AI/ML Engineer focused on building production-ready AI systems, not just models in notebooks.With ~6 years of experience in software engineering and 3+ years in AI/ML, I specialize in Generative AI, LLMs, RAG pipelines, and agentic workflows—designing systems that can reason, retrieve, and take action in real-world environments. I’ve built and deployed scalable AI applications using Python, FastAPI, Docker, and Kubernetes, with a strong focus on reliability, evaluation, and performance.My work sits at the intersection of ML engineering and systems design—from data pipelines and feature engineering to model deployment, monitoring, and continuous improvement. I’ve implemented end-to-end ML pipelines, developed LLM-powered applications, and optimized systems through better retrieval strategies, prompt design, and validation layers.I move fast, iterate quickly, and focus on what matters: delivering AI solutions that actually work in production and drive impact.What I bring:Production-grade LLM systems (RAG, agentic workflows, structured outputs)Strong Python + ML stack (TensorFlow, PyTorch, FastAPI)Experience with scalable architectures (Docker, Kubernetes, cloud)Experience in Cloud technologies: GCP, Big Query, Google Cloud Platform (GCP), IaC, TerraformFocus on evaluation, monitoring, and reliability—not just accuracyWhat I’m looking for:Roles where I can build real-world AI systems end-to-end, work on cutting-edge GenAI problems, and contribute to teams that value speed, ownership, and impact.

Experience

1. Ai Ml Engineer

   S&P Global

   Dec 2024 — Present · MI, US

   Designed and deployed scalable machine learning models using Python, TensorFlow, and Scikit-learn to analyze large financial datasets, improving market trend prediction accuracy by 18%.• Engineered robust data preprocessing and feature engineering pipelines using Pandas, NumPy, and PySpark to process financial data streams, reducing preparation time by 30%.• Built containerized FastAPI microservices integrating trained ML models into RESTful APIs and deployed them via Docker and Kubernetes on Google Cloud Platform (GCP).• Strategized CI/CD pipelines using Jenkins, GitHub, and SonarQube to automate model validation, testing, and deployment, reducing release cycle time by 25%.• Optimized ML model performance through hyperparameter tuning, cross-validation strategies, and precision-recall evaluation metrics, increasing F1 score by 12%.• Collaborated with data engineers and product teams in Agile sprints to translate financial KPIs into scalable AI-driven microservices and enterprise analytics solutions.

Education

• Lawrence Technological University

  Master of Science - MS, Information Technology

• Shri Guru Gobind Singhji Institute of Engineering and Technology, Vishnupuri, Nanded

  Bachelor of Technology (B.Tech.), computer science and engineering