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AI/ML Engineer with progressive experience designing, developing, and deploying production machine learning and Generative AI systems for risk analysis, fraud/anomaly detection, and automated decision support. Built end-to-end RAG and LLM-powered applications (document processing, summarization, semantic/knowledge search, and extraction) with grounding and evaluation in enterprise environments. Owns end-to-end GenAI/ML delivery across Azure and AWS, including MLOps workflows (MLflow, pipelines, monitoring, and deployment), and agentic multi-agent orchestration using LangGraph/LangChain with FastAPI-based secure REST APIs. Also develops analytics dashboards and scalable batch/stream data pipelines for regulated healthcare and financial domains.…AI/ML Engineer with progressive experience designing, developing, and deploying production machine learning and Generative AI systems for risk analysis, fraud/anomaly detection, and automated decision support. Built end-to-end RAG and LLM-powered applications (document processing, summarization, semantic/knowledge search, and extraction) with grounding and evaluation in enterprise environments. Owns end-to-end GenAI/ML delivery across Azure and AWS, including MLOps workflows (MLflow, pipelines, monitoring, and deployment), and agentic multi-agent orchestration using LangGraph/LangChain with FastAPI-based secure REST APIs. Also develops analytics dashboards and scalable batch/stream data pipelines for regulated healthcare and financial domains.

Sanjay Kumar Kuntala

AI Engineer, Data Scientist, Programmer, +2

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AI/ML Engineer with progressive experience designing, developing, and deploying production machine learning and Generative AI systems for risk analysis, fraud/anomaly detection, and automated decision support. Built end-to-end RAG and LLM-powered applications (document processing, summarization, semantic/knowledge search, and extraction) with grounding and evaluation in enterprise environments. Owns end-to-end GenAI/ML delivery across Azure and AWS, including MLOps workflows (MLflow, pipelines, monitoring, and deployment), and agentic multi-agent orchestration using LangGraph/LangChain with FastAPI-based secure REST APIs. Also develops analytics dashboards and scalable batch/stream data pipelines for regulated healthcare and financial domains.…AI/ML Engineer with progressive experience designing, developing, and deploying production machine learning and Generative AI systems for risk analysis, fraud/anomaly detection, and automated decision support. Built end-to-end RAG and LLM-powered applications (document processing, summarization, semantic/knowledge search, and extraction) with grounding and evaluation in enterprise environments. Owns end-to-end GenAI/ML delivery across Azure and AWS, including MLOps workflows (MLflow, pipelines, monitoring, and deployment), and agentic multi-agent orchestration using LangGraph/LangChain with FastAPI-based secure REST APIs. Also develops analytics dashboards and scalable batch/stream data pipelines for regulated healthcare and financial domains.

Available to hire

AI/ML Engineer with progressive experience designing, developing, and deploying production machine learning and Generative AI systems for risk analysis, fraud/anomaly detection, and automated decision support. Built end-to-end RAG and LLM-powered applications (document processing, summarization, semantic/knowledge search, and extraction) with grounding and evaluation in enterprise environments.

Owns end-to-end GenAI/ML delivery across Azure and AWS, including MLOps workflows (MLflow, pipelines, monitoring, and deployment), and agentic multi-agent orchestration using LangGraph/LangChain with FastAPI-based secure REST APIs. Also develops analytics dashboards and scalable batch/stream data pipelines for regulated healthcare and financial domains.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Work Experience

AI/ML Engineer at SIGNIFY HEALTH

June 1, 2025 - Present

Built production RAG and agentic AI applications for healthcare payer operations, enabling policy and prior-authorization lookup workflows. Architected containerized microservices with a FastAPI retrieval/inference layer and a KServe model-serving tier, fed by Airflow-orchestrated batch pipelines into Delta Lake. Ingested policy, formulary, prior-authorization, and provider reference data with schema/completeness validation, then prepared curated analytical layers using Databricks (PySpark) for feature generation and reporting. Designed Pinecone vector stores with document chunking and metadata filtering; implemented end-to-end semantic retrieval and prompt engineering to generate grounded member-support responses. Developed LangGraph/LangChain stateful multi-agent workflows with tool calling, conditional routing, extraction, and structured outputs for authorization-related document processing. Implemented evaluation and guardrails for grounding/relevance and monitored quality/cost/la

AI/ML Engineer (ML Engineer) at Charles Schwab

September 1, 2023 - May 1, 2025

Delivered fraud detection and risk scoring models into production on Azure ML using PyTorch/TensorFlow and a Feast feature store. Built an enterprise MLOps platform across Databricks, Snowflake, and Azure ML to standardize feature engineering, training, deployment, and monitoring with governed, reproducible lifecycles. Developed dbt transformation models for risk/transaction attributes in Snowflake and implemented Feast feature store architecture for online inference and feature governance. Built and evaluated fraud/anomaly detection models on imbalanced datasets, applied SHAP/LIME for explainability for auditability, and managed end-to-end experiment tracking and model registry workflows with MLflow and Azure ML. Deployed scalable inference services with Docker/Kubernetes and implemented CI/CD on Azure DevOps. Secured model serving endpoints with OAuth2 and Azure Key Vault, and added monitoring/drift detection with automated retraining feedback loops aligned with governance and compl

AI/ML Engineer (Data Scientist) at State of Arizona

April 1, 2022 - August 1, 2023

Developed predictive ML models using EHR, longitudinal patient history, and device data to support early identification of patient deterioration and readmission risk. Implemented streaming and batch data workflows using Kafka and Spark Streaming into Delta Lake for monitoring, anomaly detection, and alerting use cases. Prepared datasets via profiling, quality validation, deduplication, missing-value treatment, outlier analysis, normalization, and feature engineering. Created SQL-based curated datasets for outcome/treatment analysis. Built forecasting models (Prophet and ARIMA) for healthcare demand and capacity planning, and validated predictive/classification models with precision/recall/accuracy and SHAP/LIME explainability. Managed ML development with MLflow, used Docker for reproducible packaging, and delivered Power BI dashboards for model predictions and operational KPIs.

AI/ML Engineer (Python Developer – NLP) at Novartis

August 1, 2018 - December 1, 2021

Developed NLP document-processing solutions using Python, spaCy, NLTK, and scikit-learn to improve document classification accuracy and search relevance. Built preprocessing pipelines for cleaning, tokenization, normalization, and feature extraction from unstructured documents. Implemented NER models to extract entities and organizational references from documents. Engineered linguistic/statistical features to power classification/extraction models, evaluated performance using classification metrics, and refined preprocessing based on error analysis. Built Flask REST APIs to serve classification and entity extraction models, containerized applications with Docker, and deployed inference services on AWS EC2. Managed datasets and model artifacts on AWS S3 with metadata in PostgreSQL, maintaining code and documentation in Git and participating in Agile processes.

Associate Data Scientist at GRAMENER

April 1, 2016 - July 1, 2018

Built automated Python reporting for operational performance metrics across service lines, reducing manual reporting effort. Created interactive Tableau and QlikView dashboards for workforce productivity and SLA compliance insights. Developed predictive scikit-learn models to identify turnover drivers and regression models to forecast call volumes for staffing/resource allocation. Performed EDA on customer interaction data to identify recurring patterns for process improvement, automated repeated ETL steps for consistency, and supported sentiment analysis model development on post-interaction survey data. Cleaned/structured messy transactional data for statistical investigation and BI reporting, identified data quality issues in legacy systems with data stewards, and maintained documentation through variance analysis between projected and actual outcomes.