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Senior Data & ML Engineer with 12+ years of experience designing and delivering production-grade AI/ML and LLM applications, data pipelines, and reporting systems across financial services, healthcare, pharmaceuticals, and energy. Strong focus on RAG, agentic AI, MLOps, evaluation, and observability for regulated environments. Forward Deployed AI Engineer embedded with business and engineering teams to translate requirements into scalable, production-ready solutions. Proven impact includes reducing false positives, improving compliance workflows, cutting customer resolution time, and delivering low-latency agentic microservices in cloud environments.…Senior Data & ML Engineer with 12+ years of experience designing and delivering production-grade AI/ML and LLM applications, data pipelines, and reporting systems across financial services, healthcare, pharmaceuticals, and energy. Strong focus on RAG, agentic AI, MLOps, evaluation, and observability for regulated environments. Forward Deployed AI Engineer embedded with business and engineering teams to translate requirements into scalable, production-ready solutions. Proven impact includes reducing false positives, improving compliance workflows, cutting customer resolution time, and delivering low-latency agentic microservices in cloud environments.

PRASHANTH KUMAR VADAGAM

AI Engineer, Data Scientist, Data Engineer, +2

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Senior Data & ML Engineer with 12+ years of experience designing and delivering production-grade AI/ML and LLM applications, data pipelines, and reporting systems across financial services, healthcare, pharmaceuticals, and energy. Strong focus on RAG, agentic AI, MLOps, evaluation, and observability for regulated environments. Forward Deployed AI Engineer embedded with business and engineering teams to translate requirements into scalable, production-ready solutions. Proven impact includes reducing false positives, improving compliance workflows, cutting customer resolution time, and delivering low-latency agentic microservices in cloud environments.…Senior Data & ML Engineer with 12+ years of experience designing and delivering production-grade AI/ML and LLM applications, data pipelines, and reporting systems across financial services, healthcare, pharmaceuticals, and energy. Strong focus on RAG, agentic AI, MLOps, evaluation, and observability for regulated environments. Forward Deployed AI Engineer embedded with business and engineering teams to translate requirements into scalable, production-ready solutions. Proven impact includes reducing false positives, improving compliance workflows, cutting customer resolution time, and delivering low-latency agentic microservices in cloud environments.

Available to hire

Senior Data & ML Engineer with 12+ years of experience designing and delivering production-grade AI/ML and LLM applications, data pipelines, and reporting systems across financial services, healthcare, pharmaceuticals, and energy. Strong focus on RAG, agentic AI, MLOps, evaluation, and observability for regulated environments.

Forward Deployed AI Engineer embedded with business and engineering teams to translate requirements into scalable, production-ready solutions. Proven impact includes reducing false positives, improving compliance workflows, cutting customer resolution time, and delivering low-latency agentic microservices in cloud environments.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Beginner

Work Experience

Senior Python AI Engineer (Forward Deployed - Agentic AI) at Western Union

October 1, 2022 - Present

Designed and deployed production-grade AI solutions for global financial operations, including fraud detection, transaction classification, and customer risk scoring. Led a RAG solution using LangChain, FAISS, and open-source LLMs to support internal compliance Q&A across 100K+ SharePoint/Confluence documents. Engineered a GenAI customer-support assistant using fine-tuned LLaMA (LoRA) and prompt engineering, improving resolution time by 35%. Built multi-agent orchestration (planner, retriever, tool-calling) with evaluation on task success, tool-call accuracy, groundedness, and latency. Deployed agentic AI/ML microservices on Amazon EKS with Kubernetes, Helm, and autoscaling to achieve sub-second p95 latency. Implemented real-time streaming pipelines with Kafka and Spark, NLP extraction with spaCy/Hugging Face, and ML model monitoring/drift detection using EvidentlyAI and Prometheus. Delivered MLOps with Feast feature store, MLflow versioning, Airflow retraining pipelines, and productio

Senior Python ML Developer at GSK Plc / TechM

February 1, 2020 - September 30, 2022

Developed and deployed machine learning systems for clinical research and operational efficiency. Built clinical trial enrollment forecasting models improving site selection accuracy by 30%. Developed patient dropout risk models using time-series visit logs, adverse events, and adherence data. Created spaCy/SciSpaCy NER pipelines to extract drug names, trial phases, biomarkers, and adverse effects. Built multi-class classifiers for CIOMS and MedWatch safety reports using standardized MedDRA categories. Engineered PySpark/SQL feature engineering and ETL frameworks over ~100M clinical records. Containerized training/inference with Docker and deployed on Azure ML. Served models through FastAPI with automated packaging/testing/deployment and rollback using GitHub Actions and Terraform. Implemented MLflow tracking, Airflow inference workflows, and EvidentlyAI/Prometheus monitoring with automated retraining triggers. Built dashboards using Power BI.

Senior Python Developer at British Petroleum / Wipro

January 1, 2017 - January 31, 2020

Delivered scalable ML solutions for predictive maintenance, energy optimization, and real-time analytics in refinery and upstream operations. Built predictive maintenance models for pumps/turbines/compressors reducing unplanned downtime by 18%. Developed energy consumption forecasting using SCADA, weather, and demand data with XGBoost for fuel switching and grid planning. Built PySpark and Airflow ingestion pipelines processing 2 TB/day into Azure Data Lake Gen2. Implemented anomaly detection using autoencoders and Isolation Forests with alert integration. Deployed models on Azure Kubernetes Service with automated retraining triggered by drift or scheduled events. Implemented MLflow experiment tracking and registry for audit-ready MLOps. Integrated predictions into Power BI and Grafana for near-real-time decision support.

Senior Python Engineer at Novartis / Wipro

August 1, 2014 - December 31, 2017

Led development of data pipelines, automated reporting systems, and analytics platforms supporting clinical operations, regulatory compliance, and business intelligence. Designed scalable ETL pipelines using Python/Pandas/PySpark/SQL to move EDC, CTMS, and eSource data into an Azure data lake. Built data transformation frameworks for global Phase II/III clinical trial datasets. Developed automated reporting using Jinja2, Pandas, PDF generation, and scheduled email delivery producing 50+ KPIs weekly. Implemented reusable data validation and QC modules aligned with GxP audit requirements. Integrated datasets with Power BI and Tableau through APIs and Azure SQL/Snowflake. Added Pytest/Flake8, logging, and Azure DevOps CI/CD for production Python reporting systems. Ensured compliance with HIPAA, GDPR, and 21 CFR Part 11 via access controls, audit trails, and secure credential handling.

Python Data Developer at Quest Diagnostics / Wipro

April 1, 2012 - July 31, 2014

Built enterprise Python ETL and reporting systems for regulated diagnostic analytics workflows. Created ETL pipelines ingesting HL7, CSV, and XML lab data from LIS/EHR into AWS Redshift. Automated operational and clinical KPI reporting generating 100+ reports across lab locations. Built parsers and validation frameworks for LOINC/ICD-10 and patient/specimen metadata improving reporting accuracy by 40%. Developed Power BI and Tableau dashboards for capacity, backlog, turnaround time, and SLA monitoring. Implemented reconciliation logic reducing data quality issues by 70%. Automated orchestration and CI/CD using cron, Jenkins, and shell scripts with alerting via Slack/email, and supported UAT, audit reviews, lineage documentation, RBAC, and HIPAA-compliant handling.