Jeethesh Verma
I'm Jeethesh Verma, a Senior AI/ML Engineer with around 9 years of experience building production-grade machine learning systems across insurance, banking, healthcare, and enterprise SaaS.
About
I’m Jeethesh Verma, a Senior AI/ML Engineer with around 9 years of experience building production-grade machine learning systems across insurance, banking, healthcare, and enterprise SaaS. I specialize in Python, SQL, and distributed data processing, and I design scalable AI solutions that fit directly into business and product workflows.
I’ve implemented models using Scikit-learn, XGBoost, and LightGBM with a strong emphasis on feature engineering, model validation, and production-ready infrastructure. I’ve built AI-driven systems including recommendation engines, ranking models, and behavioral pipelines from large-scale event data, and I work with PyTorch for representation learning and user embeddings. I also design and operate data pipelines with PySpark and Databricks, deploy on Azure and AWS, and apply MLOps practices with MLflow to ensure reproducibility and reliability in production.
- 9 years' experience
- Joined March 2026
Industry experience
- Healthcare
- Financial Services
- Software & Internet
- Professional Services
Work experience· 5 roles
Architected and deployed enterprise-grade GenAI platforms integrated into SaaS products, enabling document intelligence, knowledge retrieval, and AI copilots on scalable, multi-tenant Azure infrastructure. Designed reusable LLM-enabled microservices and agent orchestration using MCP, built real-time agent workflows, and implemented Retrieval-Augmented Generation pipelines with Azure OpenAI, Azure AI Search, and vector databases (Pinecone). Developed scalable, asynchronous AI services with robust governance, monitoring, and cost optimization; established end-to-end MLOps pipelines (CI/CD with Azure DevOps) and model lifecycle management with MLflow. Guided cross-functional teams to deliver enterprise AI copilots embedded in product experiences.
Built scalable NLP pipelines to process large volumes of clinical and regulatory documents on AWS. Implemented ingestion and preprocessing using S3, Glue, and Lambda; extracted entities and insights with spaCy/NLP models, and deployed search capabilities with OpenSearch. Introduced early LLM-based summarization and semantic search (late 2023) and constructed batch and streaming workflows with EMR, Step Functions, and API endpoints via FastAPI.
Developed predictive models for customer behavior, credit risk, and segmentation using Scikit-learn, XGBoost, and time-based features. Implemented Monte Carlo-style uncertainty modeling, engineered feature pipelines, and created dashboards in Tableau. Deployed predictions via Flask REST APIs and collaborated with data engineers to integrate model outputs into risk and marketing workflows.
Education
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