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AI/ML Engineer with 10+ years of end-to-end experience building and deploying enterprise ML, deep learning, and Generative AI solutions across regulated industries like Banking, Healthcare, Government, and Telecom. Strong in MLOps, scalable data pipelines, and production monitoring/retraining. Hands-on with LLM-based applications (RAG, prompt engineering, fine-tuning) and NLP/CV/Time-Series systems, delivering measurable business impact through responsible AI practices and governance aligned to standards like NIST AI RMF, GDPR, and HIPAA.…AI/ML Engineer with 10+ years of end-to-end experience building and deploying enterprise ML, deep learning, and Generative AI solutions across regulated industries like Banking, Healthcare, Government, and Telecom. Strong in MLOps, scalable data pipelines, and production monitoring/retraining. Hands-on with LLM-based applications (RAG, prompt engineering, fine-tuning) and NLP/CV/Time-Series systems, delivering measurable business impact through responsible AI practices and governance aligned to standards like NIST AI RMF, GDPR, and HIPAA.

Praneeth Reddy

AI Engineer, Data Scientist, Software Engineer, +2

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AI/ML Engineer with 10+ years of end-to-end experience building and deploying enterprise ML, deep learning, and Generative AI solutions across regulated industries like Banking, Healthcare, Government, and Telecom. Strong in MLOps, scalable data pipelines, and production monitoring/retraining. Hands-on with LLM-based applications (RAG, prompt engineering, fine-tuning) and NLP/CV/Time-Series systems, delivering measurable business impact through responsible AI practices and governance aligned to standards like NIST AI RMF, GDPR, and HIPAA.…AI/ML Engineer with 10+ years of end-to-end experience building and deploying enterprise ML, deep learning, and Generative AI solutions across regulated industries like Banking, Healthcare, Government, and Telecom. Strong in MLOps, scalable data pipelines, and production monitoring/retraining. Hands-on with LLM-based applications (RAG, prompt engineering, fine-tuning) and NLP/CV/Time-Series systems, delivering measurable business impact through responsible AI practices and governance aligned to standards like NIST AI RMF, GDPR, and HIPAA.

Available to hire

AI/ML Engineer with 10+ years of end-to-end experience building and deploying enterprise ML, deep learning, and Generative AI solutions across regulated industries like Banking, Healthcare, Government, and Telecom. Strong in MLOps, scalable data pipelines, and production monitoring/retraining.

Hands-on with LLM-based applications (RAG, prompt engineering, fine-tuning) and NLP/CV/Time-Series systems, delivering measurable business impact through responsible AI practices and governance aligned to standards like NIST AI RMF, GDPR, and HIPAA.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Intermediate

Language

English

Advanced

Work Experience

Sr AI/ML Engineer at JPMorgan Chase & Co

June 1, 2024 - Present

Architected and led an enterprise Generative AI platform using AWS Bedrock and LLMs (Claude, GPT-4) to power conversational banking assistants, automating millions of customer interactions and reducing handling time. Built fraud detection systems (XGBoost, GNNs, isolation forests) at high transaction throughput and improved alert quality, saving annual fraud losses. Developed credit risk scoring using gradient boosting and survival analysis for delinquency prediction. Implemented AML-focused NLP pipelines with FinBERT, sentence embeddings, and knowledge graphs. Delivered RAG chatbot solutions indexing large policy corpora and reducing resolution time dramatically. Established an MLOps platform on Kubernetes using MLflow, Kubeflow Pipelines, Airflow, and Terraform; implemented model monitoring and drift detection at scale using Evidently, Prometheus, and Grafana. Added responsible AI guardrails (SHAP, Fairlearn, Captum) for regulatory submissions. Built and deployed real-time check-imag

AI/ML Engineer (Consultant) at State of Alabama

March 1, 2022 - May 31, 2024

Delivered a tax-fraud-detection platform for the Franchise Tax Board using XGBoost/anomaly detection/entity resolution on millions of returns to identify fraudulent refunds. Built Medicad eligibility and provider network fraud detection supporting recoveries. Designed NLP pipelines to classify and route large volumes of citizen-service requests across agencies with improved accuracy and SLAs. Implemented unemployment-insurance fraud detection during peak demand and developed a citizen-facing multilingual chatbot using AWS Lex/Lambda/DynamoDB. Built fairness-aware models for child-welfare prioritization and designed OCR+NLP document processing pipelines to digitize legacy records. Architected secure FedRAMP-aligned AWS GovCloud data lakes and standardized model deployment using SageMaker Pipelines, MLflow, Step Functions, and CodePipeline, reducing time-to-production. Created wildfire risk and forecasting/capacity planning models; authored Responsible AI and governance documentation a

Senior Machine Learning Engineer at Optum

June 1, 2021 - February 28, 2022

Built clinical NLP pipelines on Azure ML to extract diagnoses/medications/procedures from large EHR corpora using BioClinicalBERT, MedSpaCy, and cTAKES; improved clinical coding accuracy. Developed hospital readmission prediction models using XGBoost and LSTM and reduced readmissions. Implemented fraud-waste-and-abuse detection using autoencoders and gradient boosting across large claims datasets. Built HIPAA-compliant medical image classification for chest X-rays using ResNet-50 and ViT. Created uplift/propensity models for outreach campaigns and automated prior authorization decision support. Implemented patient risk stratification using Cox models and survival deep learning approaches. Architected HIPAA-compliant data lake ingestion and de-identification pipelines from HL7/FHIR/X12 and CDA. Led adoption of Azure ML Pipelines and CI/CD via MLflow and Azure DevOps across production models. Developed clinical-trial patient matching using Sentence-BERT embeddings with FAISS, and impleme

Machine Learning Engineer at Verizon Communications

July 1, 2018 - May 31, 2020

Developed churn prediction using XGBoost and deep learning on large subscriber datasets and improved prediction quality while reducing voluntary churn. Built next-best-offer recommendations using deep learning and contextual bandits, increasing conversions. Designed network anomaly detection deployed on Kafka + Spark Streaming with sub-second latency. Built predictive maintenance models for cell-tower equipment using time-series forecasting and survival analysis, reducing outages and field costs. Created call-center NLP analytics from speech-to-text, sentiment, and topic modeling to surface customer pain points. Implemented fiber deployment optimization using geospatial ML and demand forecasting. Built SIM-swap/account-takeover fraud detection models using gradient boosting and behavioral biometrics. Designed network-slicing optimization with reinforcement learning. Engineered reusable feature stores and customer-360 profiles integrating CRM, billing, network, and clickstream data. Imp

Data Scientist at Cognizant Technology Solutions

July 1, 2015 - March 31, 2018

Built IT incident classification and auto-routing using NLP and transformer-based approaches on ServiceNow tickets to automate L1 triage and reduce resolution time. Developed application log anomaly detection systems using LSTM autoencoders and clustering to detect outages earlier and reduce downtime. Implemented an IT helpdesk automation chatbot using Rasa/Dialogflow with intent classification, deflecting tickets and saving agent-hours. Created time-series forecasting and reinforcement learning for predictive capacity planning and cloud cost optimization. Built software defect prediction from GitHub commit features and improved defect outcomes. Developed employee attrition prediction with explainable AI (SHAP) to identify retention risks. Built sentiment analysis and brand monitoring dashboards using NLP and topic modeling, and developed recommendation engines for e-commerce. Engineered large-scale data lakes using Hadoop/Spark/Hive and established MLOps with Jenkins/Docker/Airflow to