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AI/ML Engineer with ~4 years of experience building and deploying scalable machine learning and generative AI systems across financial services and e-commerce. Skilled in Python, PyTorch, SQL, LangChain, LangGraph, and distributed data processing with Kafka and Spark. Experienced in fraud detection, NLP-driven automation, recommendation systems, and agentic/LLM applications using RAG. Strong MLOps background building production-grade, low-latency ML systems with Docker, Kubernetes, and AWS, including real-time inference services and end-to-end ML lifecycle practices such as monitoring and CI/CD.…AI/ML Engineer with ~4 years of experience building and deploying scalable machine learning and generative AI systems across financial services and e-commerce. Skilled in Python, PyTorch, SQL, LangChain, LangGraph, and distributed data processing with Kafka and Spark. Experienced in fraud detection, NLP-driven automation, recommendation systems, and agentic/LLM applications using RAG. Strong MLOps background building production-grade, low-latency ML systems with Docker, Kubernetes, and AWS, including real-time inference services and end-to-end ML lifecycle practices such as monitoring and CI/CD.

Akhil Bhimarasetty

AI Engineer, Data Scientist, Software Engineer, +2

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AI/ML Engineer with ~4 years of experience building and deploying scalable machine learning and generative AI systems across financial services and e-commerce. Skilled in Python, PyTorch, SQL, LangChain, LangGraph, and distributed data processing with Kafka and Spark. Experienced in fraud detection, NLP-driven automation, recommendation systems, and agentic/LLM applications using RAG. Strong MLOps background building production-grade, low-latency ML systems with Docker, Kubernetes, and AWS, including real-time inference services and end-to-end ML lifecycle practices such as monitoring and CI/CD.…AI/ML Engineer with ~4 years of experience building and deploying scalable machine learning and generative AI systems across financial services and e-commerce. Skilled in Python, PyTorch, SQL, LangChain, LangGraph, and distributed data processing with Kafka and Spark. Experienced in fraud detection, NLP-driven automation, recommendation systems, and agentic/LLM applications using RAG. Strong MLOps background building production-grade, low-latency ML systems with Docker, Kubernetes, and AWS, including real-time inference services and end-to-end ML lifecycle practices such as monitoring and CI/CD.

Available to hire

AI/ML Engineer with ~4 years of experience building and deploying scalable machine learning and generative AI systems across financial services and e-commerce. Skilled in Python, PyTorch, SQL, LangChain, LangGraph, and distributed data processing with Kafka and Spark. Experienced in fraud detection, NLP-driven automation, recommendation systems, and agentic/LLM applications using RAG.

Strong MLOps background building production-grade, low-latency ML systems with Docker, Kubernetes, and AWS, including real-time inference services and end-to-end ML lifecycle practices such as monitoring and CI/CD.

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Experience Level

Expert

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Intermediate

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Intermediate

Language

Work Experience

AI/ML Engineer at Mastercard

July 1, 2025 - Present

Designed a PyTorch-based multi-modal anomaly detection system for global payment networks, improving fraud detection accuracy by 26%. Built a real-time streaming pipeline using Apache Spark to ingest and process high-volume transaction events for sub-second anomaly scoring on 2M+ transactions/day under strict latency constraints. Developed ML models for system-level anomaly detection across transaction, merchant, and device behavioral signals. Implemented LLM-powered feature extraction workflows using OpenAI APIs and LangChain to transform unstructured transaction logs and dispute narratives into structured fraud signals, reducing manual investigation effort by 38%. Engineered embedding-based similarity search pipelines for hidden fraud relationship detection, improving recall by 32%. Deployed scalable inference services with FastAPI, Docker, Kubernetes, and AWS achieving 99.9% uptime and sub-180ms latency. Assisted in building agentic AI workflows with LangChain to generate contextual

Machine Learning Scientist at Wells Fargo

October 1, 2024 - June 30, 2025

Built an NLP-based customer support intelligence system using Python and Hugging Face Transformers (BERT) to classify and route chat/email queries into intent categories, improving intent detection accuracy by 24%. Created supervised text classification and sentiment analysis models using transformer embeddings and traditional ML classifiers to prioritize high-impact tickets, reducing manual triage effort by 35%. Implemented a retrieval-augmented generation (RAG) pipeline using enterprise knowledge base embeddings and vector search to reduce incorrect support resolutions by 28%. Developed LLM-based query understanding workflows using prompt engineering to enhance intent extraction and domain-specific response accuracy, improving chatbot performance by 30%. Architected scalable FastAPI microservices for production NLP model deployment supporting 5,000+ requests/min under <200ms latency. Established real-time streaming pipelines with Apache Kafka for continuous data flow into ML systems

Machine Learning Engineer at Accenture

April 1, 2021 - July 31, 2023

Built a Python-based recommendation system using PyTorch/TensorFlow with Scikit-learn collaborative filtering on SQL-based user behavioral datasets (clickstream, purchase history, search logs), improving recommendation relevance by 28%. Developed hybrid models combining supervised ranking (XGBoost, Random Forest) with unsupervised clustering (K-Means) for user segmentation, increasing CTR by 22% across e-commerce platforms. Created AWS-based scalable ML pipelines using S3, Lambda, and SageMaker integrated with Pandas ETL workflows, enabling automated training/batch inference/retraining cycles and reducing retraining time by 40%. Applied NLP techniques using TF-IDF and deep embeddings for semantic matching between user queries and product catalog metadata. Orchestrated large-scale feature engineering pipelines using Pandas and NumPy to capture behavioral signals, improving model performance by 18%. Produced Power BI dashboards for KPI tracking, increasing engagement by 12%. Implemented