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Senior Generative AI Engineer with 11+ years of experience delivering enterprise-scale GenAI, AI/ML, data science, and cloud-native platforms across retail, healthcare, insurance, government, and travel. Architected and deployed LLM/RAG and agentic workflows using leading cloud and LLM frameworks. Built scalable, secure, and measurable AI solutions using AWS, Azure, and GCP, with strong focus on MLOps/LLMOps, observability, evaluation, and production reliability. Experienced in end-to-end data engineering, predictive modeling, and integrating AI capabilities into microservices and enterprise systems.…Senior Generative AI Engineer with 11+ years of experience delivering enterprise-scale GenAI, AI/ML, data science, and cloud-native platforms across retail, healthcare, insurance, government, and travel. Architected and deployed LLM/RAG and agentic workflows using leading cloud and LLM frameworks. Built scalable, secure, and measurable AI solutions using AWS, Azure, and GCP, with strong focus on MLOps/LLMOps, observability, evaluation, and production reliability. Experienced in end-to-end data engineering, predictive modeling, and integrating AI capabilities into microservices and enterprise systems.

ASHOK CH

AI Engineer, Data Scientist, Software Engineer, +4

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Senior Generative AI Engineer with 11+ years of experience delivering enterprise-scale GenAI, AI/ML, data science, and cloud-native platforms across retail, healthcare, insurance, government, and travel. Architected and deployed LLM/RAG and agentic workflows using leading cloud and LLM frameworks. Built scalable, secure, and measurable AI solutions using AWS, Azure, and GCP, with strong focus on MLOps/LLMOps, observability, evaluation, and production reliability. Experienced in end-to-end data engineering, predictive modeling, and integrating AI capabilities into microservices and enterprise systems.…Senior Generative AI Engineer with 11+ years of experience delivering enterprise-scale GenAI, AI/ML, data science, and cloud-native platforms across retail, healthcare, insurance, government, and travel. Architected and deployed LLM/RAG and agentic workflows using leading cloud and LLM frameworks. Built scalable, secure, and measurable AI solutions using AWS, Azure, and GCP, with strong focus on MLOps/LLMOps, observability, evaluation, and production reliability. Experienced in end-to-end data engineering, predictive modeling, and integrating AI capabilities into microservices and enterprise systems.

Available to hire

Senior Generative AI Engineer with 11+ years of experience delivering enterprise-scale GenAI, AI/ML, data science, and cloud-native platforms across retail, healthcare, insurance, government, and travel. Architected and deployed LLM/RAG and agentic workflows using leading cloud and LLM frameworks.

Built scalable, secure, and measurable AI solutions using AWS, Azure, and GCP, with strong focus on MLOps/LLMOps, observability, evaluation, and production reliability. Experienced in end-to-end data engineering, predictive modeling, and integrating AI capabilities into microservices and enterprise systems.

Skills

Experience Level

Expert

Expert

Expert

Expert

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Expert

Expert

Expert

Expert

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Expert

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Work Experience

Senior Generative AI Engineer at The TJX Companies

March 1, 2024 - Present

Architected and deployed an enterprise Retail AI Copilot using Amazon Bedrock and Claude/Nova with RAG to reduce SKU research and merchandising analysis time by 42%. Built a cloud-native platform on EKS/Lambda/API Gateway/OpenSearch/S3/Kafka supporting merchandising, pricing, and inventory planning workflows. Designed enterprise data ingestion and distributed ETL using Glue/PySpark/dbt, centralized repositories in Redshift/PostgreSQL/DynamoDB/S3, and semantic retrieval using Titan embeddings, Pinecone, OpenSearch, and Bedrock Knowledge Bases. Developed grounded RAG pipelines with LangChain and Bedrock KBs, optimized prompts and retrieval (chunking, reranking, hybrid search), and implemented multi-agent orchestration using LangGraph/CrewAI/MCP/tool-calling. Established evaluation and governance using Promptfoo, Ragas, Evidently AI and monitoring/observability with CloudWatch/Prometheus/Grafana/Datadog/MLflow. Delivered production microservices via FastAPI, containerized on ECR, and auto

Senior AI/ML Engineer at Mayo Clinic

December 1, 2022 - February 29, 2024

Delivered a Generative AI–powered Clinical Decision Support Assistant improving clinical decision accuracy by 24%. Built a cloud-native healthcare AI platform using Vertex AI/Gemini/LangChain with RAG, deployed on GKE with supporting services (Cloud Functions, BigQuery, Cloud Storage, Pub/Sub). Implemented HIPAA-aligned data ingestion and integration from FHIR/HL7 and Epic EHR via pipelines into BigQuery/Cloud SQL/PostgreSQL/Cloud Storage. Designed semantic search and RAG using Vertex AI embeddings/search and Pinecone to improve contextual retrieval and reduce hallucinations. Developed predictive ML models (TensorFlow, Scikit-learn, XGBoost, Random Forest) with explainability (SHAP/LIME) and physician feedback. Built FastAPI microservices, containerized workloads with Docker/Artifact Registry, automated deployments with Terraform/MLflow/Vertex AI Pipelines, and added observability using Cloud Monitoring/Logging/Prometheus/Grafana/Datadog/Evidently AI.

OHAI/ML Engineer at State of Ohio

July 1, 2021 - November 30, 2022

Architected a cloud-native Fraud Detection and Predictive Analytics platform on Azure, improving fraud detection accuracy by 38% while reducing false positives. Built an AI platform using Azure Machine Learning, AKS, Functions, Synapse Analytics, Blob Storage, Event Hubs, and API Management. Implemented enterprise data ingestion from claims, policies, payments and REST systems into Azure data stores and streaming pipelines. Built distributed ETL with Azure Data Factory/Azure Databricks/PySpark/pandas/SQL, designed centralized repositories in Synapse/SQL/Cosmos/Blob, and created reusable feature engineering pipelines for risk indicators and fraud signals. Developed and optimized ML models (TensorFlow, Scikit-learn, XGBoost, Random Forest, Logistic Regression) with evaluation, tuning, cross-validation, SHAP explanations, and offline/statistical validation. Containerized inference workloads on AKS, automated CI/CD using Azure DevOps and Terraform, and implemented monitoring via Azure Moni

Machine Learning Engineer at Verisk

December 1, 2019 - June 30, 2021

Improved underwriting accuracy by 29% and reduced claims processing time by building an insurance claims intelligence platform using Python/TensorFlow/Scikit-learn/XGBoost and distributed processing with Spark. Engineered cloud-native services on AWS (ECS/EC2/S3/Redshift/Kafka) with REST APIs to enable scalable claims processing, predictive risk assessment, and underwriting analytics. Built ingestion pipelines integrating policy/claims/customer/telematics and external databases/APIs, created distributed ETL with Spark/Airflow/Hive, and designed centralized data repositories in Redshift/PostgreSQL/SQL Server/Snowflake/S3. Developed feature engineering pipelines and predictive models for claims severity/fraud/policy risk; implemented IDP solutions using BERT/spaCy/NLTK for classification/entity extraction. Validated models using SHAP/A-B testing/offline evaluation, containerized services for reliable inference, and automated CI/CD with Jenkins/CloudFormation/MLflow. Added monitoring with

Data Scientist at Google

January 1, 2018 - September 30, 2019

Architected an intelligent search and recommendation platform improving search relevance by 34% and increasing user engagement. Built analytics infrastructure on GCP using Cloud Storage/BigQuery/Dataflow/Spark/Pub/Sub and REST APIs for scalable search analytics and recommendation. Developed ingestion pipelines from search logs/clickstream/user profiles and Google Analytics into BigQuery/Cloud Storage and streaming systems. Performed EDA and feature engineering for personalization, developed predictive models (TensorFlow/Scikit-learn/XGBoost/Logistic Regression/Random Forest) to improve ranking and recommendation, and built reusable feature pipelines. Validated via A/B testing and statistical significance against KPIs. Deployed ML/analytics workloads using Dataflow/Spark/BigQuery, automated CI/CD with Jenkins/Build, and used Infrastructure as Code and monitoring/observability with Cloud Monitoring/ELK/Grafana.

Software Engineer at MakeMyTrip

July 1, 2015 - December 31, 2017

Improved booking recommendations by 26% by building a travel analytics and recommendation platform using Python/Java/SQL/Scikit-learn and Spark, incorporating customer-behavior analysis and personalized modeling. Engineered a scalable platform on Hadoop/Hive/Spark with MySQL and Kafka for enterprise reporting, booking analytics, and decision-making via REST APIs. Built ingestion pipelines integrating airline/hotel APIs, payments, and customer/profile data into centralized repositories. Conducted EDA and feature engineering for trends/seasonality/pricing and cancellation patterns, and built predictive models for booking, segmentation, demand forecasting, and recommendations. Optimized models with tuning/cross-validation and validated through A/B testing and statistical significance. Developed modular services (Python/Java/Spring Boot/MVC) with CI/CD automation and production support for Hadoop/Hive clusters; managed stability and performance monitoring.