Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

AI/ML Engineer with 4+ years building production-grade machine learning and Generative AI solutions in healthcare and enterprise environments. Strong in Python, PyTorch, Scikit-learn, XGBoost, Apache Spark/PySpark, SQL, and feature engineering, with hands-on experience in LLMs and RAG. Experienced in end-to-end ML and GenAI pipelines—data preprocessing, model training, evaluation, deployment, and monitoring—using MLflow, FastAPI, Docker, Kubernetes, and AWS services (SageMaker/Bedrock). Proven track record translating data into scalable AI products that improve automation and decision support.…AI/ML Engineer with 4+ years building production-grade machine learning and Generative AI solutions in healthcare and enterprise environments. Strong in Python, PyTorch, Scikit-learn, XGBoost, Apache Spark/PySpark, SQL, and feature engineering, with hands-on experience in LLMs and RAG. Experienced in end-to-end ML and GenAI pipelines—data preprocessing, model training, evaluation, deployment, and monitoring—using MLflow, FastAPI, Docker, Kubernetes, and AWS services (SageMaker/Bedrock). Proven track record translating data into scalable AI products that improve automation and decision support.

Raghu Satram

AI Engineer, Data Scientist, Data Engineer, +4





AI/ML Engineer with 4+ years building production-grade machine learning and Generative AI solutions in healthcare and enterprise environments. Strong in Python, PyTorch, Scikit-learn, XGBoost, Apache Spark/PySpark, SQL, and feature engineering, with hands-on experience in LLMs and RAG. Experienced in end-to-end ML and GenAI pipelines—data preprocessing, model training, evaluation, deployment, and monitoring—using MLflow, FastAPI, Docker, Kubernetes, and AWS services (SageMaker/Bedrock). Proven track record translating data into scalable AI products that improve automation and decision support.…AI/ML Engineer with 4+ years building production-grade machine learning and Generative AI solutions in healthcare and enterprise environments. Strong in Python, PyTorch, Scikit-learn, XGBoost, Apache Spark/PySpark, SQL, and feature engineering, with hands-on experience in LLMs and RAG. Experienced in end-to-end ML and GenAI pipelines—data preprocessing, model training, evaluation, deployment, and monitoring—using MLflow, FastAPI, Docker, Kubernetes, and AWS services (SageMaker/Bedrock). Proven track record translating data into scalable AI products that improve automation and decision support.

Available to hire

AI/ML Engineer with 4+ years building production-grade machine learning and Generative AI solutions in healthcare and enterprise environments. Strong in Python, PyTorch, Scikit-learn, XGBoost, Apache Spark/PySpark, SQL, and feature engineering, with hands-on experience in LLMs and RAG.

Experienced in end-to-end ML and GenAI pipelines—data preprocessing, model training, evaluation, deployment, and monitoring—using MLflow, FastAPI, Docker, Kubernetes, and AWS services (SageMaker/Bedrock). Proven track record translating data into scalable AI products that improve automation and decision support.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Beginner

Beginner

Beginner

Work Experience

AI/ML Engineer at DXC Technology

January 1, 2025 - Present

Developed and deployed production ML solutions using Python, PyTorch, Scikit-learn, PySpark, and SQL across healthcare datasets with 20M+ records to support risk stratification and predictive analytics. Built reusable preprocessing and feature engineering pipelines using Python, PySpark, Databricks, and dbt, generating 300+ predictive features for recurring forecasting and risk modeling workflows. Developed RAG applications using AWS Bedrock, LangChain, LangGraph, Pinecone, and FAISS to reduce manual enterprise document review effort by 60%. Implemented agentic AI workflows for claims lookup and document retrieval, reducing analyst research time by 45%. Productionized LLM/ML inference services using AWS SageMaker, MLflow, FastAPI, Docker, and REST APIs, reducing deployment cycles by 40% and improving reproducibility. Established model monitoring and data validation for drift, latency, quality, and feature integrity, reducing incident resolution time by 35%. Collaborated cross-functiona

AI/ML Engineer at Zensar Technologies

December 1, 2020 - July 31, 2023

Developed supervised machine learning models using Python, Scikit-learn, XGBoost, Random Forest, and Gradient Boosting across 10M+ enterprise records, improving prediction accuracy by 22%. Engineered reusable preprocessing and feature engineering pipelines using Python, SQL, PySpark, and Apache Spark, reducing feature preparation time by 35%. Optimized distributed training and feature processing using Apache Spark and PySpark, reducing training/data preparation time by 45% for workloads across 50M+ records. Built anomaly detection and predictive analytics models using statistical and ML techniques, improving early issue detection by 30% while reducing false positives by 20%. Built scalable ETL pipelines processing 5M+ records daily and delivering validated datasets for analytics and downstream applications. Implemented MLflow tracking, Docker containerization, model versioning, and GitHub Actions CI/CD to reduce model release cycles by 40%. Developed REST-based ML inference services an