Open to opportunities

Lokeshwar Kodipunjula

@lokeshwarkodipunjula

Message

AI/ML engineer building scalable NLP, AIOps, and ML-driven full-stack solutions for real-time systems.

United States

Message

What I'm looking for

I seek a role where I can build and operationalize ML/NLP systems end-to-end, collaborate with cross-functional teams in Agile environments, and drive MLOps automation and observability at scale.

I am an AI/ML engineer with 4+ years of experience designing and deploying production-grade machine learning and NLP solutions across insurance, healthcare, and fintech domains. I build supervised, unsupervised, and deep learning models and operationalize them as RESTful microservices using FastAPI, Docker, and Kubernetes.

My work focuses on real-time and batch data pipelines (PySpark, Kafka, Flink), MLOps automation (Airflow, MLflow, GitHub Actions), and observability (Prometheus, Grafana, Datadog) to reduce MTTR and improve model reliability. I have integrated models into full-stack applications using Node.js/Express and React, delivering investigator dashboards and fraud/clinical risk platforms.

I collaborate closely with SRE, DevOps, product, and engineering teams in Agile environments to drive end-to-end architecture, automate retraining and drift detection, and implement secure, compliant deployments on cloud platforms such as AWS and Azure.

Experience

Work history, roles, and key accomplishments

Current

AI-ML Engineer

Current

AIG

Nov 2024 - Present (1 year 7 months)

Designed and deployed fraud-scoring microservices and full-stack investigator dashboards, improving risk-scoring accuracy and enabling near-real-time fraud insights via Kafka→PySpark pipelines and monitored containerized services.

Python fastAPI XGBoost Kafka Pyspark Docker Kubernetes Prometheus

Python Data Scientist

VNS Health

Jan 2024 - Oct 2024 (9 months)

Built clinical NLP pipelines and patient-risk models using spaCy and XGBoost, standardized EHR data and deployed Dockerized FastAPI inference services on Azure to enable real-time clinical scoring and reproducible ML releases.

Python SpaCy XGBoost fastAPI MLFlow Airflow Docker Azure SQL Tableau

Jr. ML Engineer

Axis Bank

Jul 2021 - Aug 2023 (2 years 1 month)

Developed real-time transaction fraud detection models and streaming pipelines using Kafka and Flink, engineered PySpark features to improve scoring accuracy, and automated retraining and monitoring via MLflow and Airflow.

Python scikit learn XGBoost Pyspark Kafka Apache Flink MLFlow Airflow Docker Kubernetes

Education

Degrees, certifications, and relevant coursework

University of Texas at Arlington

Master of Science, Artificial Intelligence / Machine Learning

Completed a master's degree program focused on advanced AI/ML techniques, applied to scalable machine learning and NLP solutions in industry settings.

Kingston Engineering College

Bachelor of Engineering, Engineering

Completed a bachelor's engineering degree providing foundational training in computer science and engineering principles relevant to ML and software development.