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AKHILESH VENIGALLA

@akhileshvenigalla

Message

AI/ML Engineer building scalable ML, GenAI, and MLOps for measurable impact.

United States

Message

What I'm looking for

I’m looking for an AI/ML role where I can build production-ready GenAI and ML systems with strong MLOps—end-to-end pipelines, real-time APIs, monitoring, and A/B testing—to deliver measurable latency/accuracy gains with close stakeholder collaboration.

I’m an AI/ML Engineer with 5+ years of experience designing and deploying scalable machine learning, deep learning, and Generative AI solutions. I’ve driven outcomes like reducing data latency by 40% and improving model accuracy by 22% through advanced MLOps, A/B testing, and distributed computing. In production work at T-Mobile, I built churn and segmentation models that improved retention analysis accuracy by 15% and supported targeted engagement for 1M+ subscribers.

I focus on end-to-end delivery: architecting data pipelines (PySpark, Kafka), serving real-time inference via REST APIs (FastAPI, <100ms latency), and maintaining reliability with continuous monitoring and validation (95%+ prediction accuracy, p<0.05). I also build LLM-powered systems—RAG initiatives using Pinecone/FAISS and prompt engineering strategies to reduce hallucinations—and collaborate closely with stakeholders, mentored junior team members, and communicated insights through technical documentation and dashboards.

Experience

Work history, roles, and key accomplishments

Current

AI/ML Engineer

Current

T-Mobile

Jun 2025 - Present (1 year)

Built and deployed churn and sentiment models in TensorFlow/XGBoost, improving retention analysis accuracy by 15% and reducing manual ticket classification time by 20% for 1M+ subscribers. Architected PySpark+Kafka pipelines that cut data processing latency by 40%, and delivered FastAPI inference APIs with <100ms latency while maintaining 95%+ prediction accuracy through A/B testing and drift moni

TensorFlow XGBoost Pyspark Kafka fastAPI MLFlow A B Testing RAG Prompt Engineering MLOps

Data Scientist

Wipro

Jun 2022 - Dec 2023 (1 year 6 months)

Developed churn, credit risk, and fraud detection models using TensorFlow and Scikit-learn, improving prediction accuracy by 22% and reducing annual financial risk exposure by $500K. Built Airflow/Kafka/SQL ETL pipelines cutting data processing time by 35% (saving 12 hours/week) and implemented clustering, anomaly detection, and forecasting to improve conversions by 18% and reduce false positives

TensorFlow scikit learn XGBoost PyTorch Airflow Kafka SQL Clustering Anomaly Detection ETL

Junior Data Scientist

Wipro

Jun 2019 - May 2022 (2 years 11 months)

Supported development of classification, regression, and prediction models by performing data cleaning, preprocessing, and feature engineering in Python/Scikit-learn and SQL. Assisted with customer segmentation and OpenCV-based computer vision (image processing/face recognition), and built automated dashboard and data-visualization pipelines for internal reporting.

Python SQL scikit learn Data Cleaning Feature Engineering Clustering OpenCV Data Visualization