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Vetted Data Scientists in the NYC Metro
Pre-screened and vetted in the NYC Metro.
Nan Xiao
Junior AI Engineer specializing in LLMs, RAG, and agent evaluation
Lokeshwar Kodipunjula
Mid-level AI/ML Engineer specializing in NLP, fraud detection, and MLOps
“LLM/ML platform engineer with hands-on experience taking an LLM document summarization prototype into a production-grade service on AWS EKS, emphasizing low-latency inference, drift monitoring, and safe CI/CD rollouts (canary + rollback). Strong in real-time debugging of agentic/RAG systems (tracing, retrieval/index drift fixes) and in developer enablement through practical workshops (Docker/Kubernetes/FastAPI) plus pre-sales support via demos and benchmarks to close pilots.”
Jareena kowsar shaik
Mid-level Machine Learning & GenAI Engineer specializing in LLMs, RAG, and NLP
“Built and deployed an LLM-powered customer support assistant (“Notable Assistant”) focused on automating common post-customer queries while maintaining multi-turn context and meeting scalability/latency needs. Experienced with production orchestration and operations using Kubernetes and Apache Airflow (DAG-based ETL, scheduling, monitoring/alerts), and has partnered closely with customer service stakeholders to align chatbot behavior with brand voice through iterative testing.”
Sai Kamuganti
Senior AI/ML Engineer specializing in Generative AI, RAG, and LLM fine-tuning
Vamshidar Bhootkuri
Mid-level Data Scientist specializing in credit risk and fraud analytics
Adithya Bandari
Mid-level Data Scientist specializing in AI/ML and Generative AI
Neel Adalja
Mid-level Data Scientist/Data Engineer specializing in ML, MLOps, and real-time data pipelines
Shylesh Bontha
Mid-level Data Scientist specializing in healthcare and financial risk modeling
Yukta Chikate
Mid-level Machine Learning Engineer specializing in safety-critical and uncertainty-aware ML systems
“Built and productionized an LLM-powered assistant for company documents and support questions, focused on reducing time spent searching PDFs/policies/tickets while preventing hallucinations by grounding answers in approved sources. Demonstrates strong production engineering (Kubernetes/orchestration, caching, monitoring, fallbacks) plus security-minded permissioning and close collaboration with operations/support stakeholders.”
Ojasmitha Pedirappagari
Mid-level AI Engineer specializing in LLMs, RAG, and agentic platforms
“Built and shipped a production RAG-based assistant that lets parents ask natural-language questions about their child’s learning progress, using pgvector retrieval (child-id filtered) and Redis caching to hit ~180ms latency. Implemented real-world guardrails and compliance (Llama Guard, COPPA, retrieval thresholds, fallbacks) with 99.5% uptime, and ran human-in-the-loop eval loops that improved satisfaction from 3.8 to 4.2 while serving 60k+ monthly users and reducing costs significantly.”
Thrinesh Thode
Mid-level AI/ML Engineer specializing in MLOps and LLM applications
“BNY Mellon engineer who has built and operated production AI systems end-to-end: a LangChain/Pinecone RAG platform scaled via FastAPI + Kubernetes to 1000 RPM with 99.9% uptime, supported by monitoring and data-drift detection. Also deep in data/infra orchestration (Airflow, Dagster, Terraform on AWS/EMR/EC2), processing 500GB+ daily and delivering measurable reliability and performance gains, plus strong compliance-facing model explainability using SHAP and Tableau.”
Ashtik Mahapatra
Mid-level Data Scientist specializing in LLMs, RAG, and document intelligence
“LLM/ML engineer who has shipped production systems in legal/financial-risk domains at Wolters Kluwer, including a hybrid OCR+deterministic+LLM extraction pipeline that structured UCC filings at massive scale and drove $6M+ in revenue. Also built LangGraph-based multi-agent “Deep Research” workflows with model routing, tool calls (MCP), persistence, and human-in-the-loop review, and partnered closely with policy writers to deliver LLM summarization that cut writing time by ~60%.”
Tejaswini Narayana
Mid-level Data Scientist & AI/ML Engineer specializing in GenAI and cloud ML
“GenAI/LLM engineer who recently built a production compliance assistant at State Farm for KYC/AML and regulatory teams, using AWS Bedrock + LangChain with Textract/Lambda pipelines to extract fields, tag risk, and summarize long documents. Implemented RAG, strict structured outputs, and human-in-the-loop guardrails, and reports automating ~80% of documentation work while reducing review time by ~40%.”
Ajay Chauhan
Senior Backend/Cloud Developer specializing in Python and AWS-native data workflows
Anish Reddy
Mid-level Data Scientist specializing in ML, NLP/LLMs, and fraud & risk analytics
Tanay Mevada
Mid-level Data Scientist specializing in GenAI, NLP, and MLOps
Durga Bhavani Prasad Menda
Mid-level Data Scientist specializing in LLMs, NLP, and predictive modeling in healthcare and finance
Gopi Chand RAVIPATI
Mid-level Data Scientist specializing in GenAI, RAG, and multi-agent orchestration
Anish Reddy
Mid-level Data Scientist specializing in ML, NLP, and fraud detection
Hayagreeva Desineni
Mid-level Data Scientist specializing in NLP, deep learning, and Generative AI
Dhileep Pagadala
Mid-level Data Scientist specializing in LLMs and applied machine learning
Kishan Patel
Mid-level Data Scientist specializing in ML, NLP, and forecasting across finance and retail
Poojitha Vajja
Mid-level Data Scientist / ML Engineer specializing in healthcare predictive analytics and NLP
“Built and deployed a real-time hospital readmission risk prediction system at NYU Langone Health, combining structured EHR data with BERT-based NLP on clinical notes and serving predictions to clinicians via Azure ML and FHIR APIs. Emphasizes production reliability and clinical trust through SHAP-based explainability and robust healthcare data preprocessing, and reports a 22% reduction in 30-day readmissions.”
shubham patil
Mid-level AI Engineer specializing in Generative AI, RAG systems, and fraud analytics
“Built and deployed a RAG-based student/faculty support chatbot at a university that answers from official syllabus/policy documents and now supports 4,000+ students while reducing repetitive support requests. Hands-on with LangChain, LangGraph, and CrewAI to orchestrate reliable agentic workflows, with a strong focus on testing/monitoring in production and cross-functional delivery (e.g., marketing analytics automation at Steve Madden).”