Vetted Machine Learning Engineers in the NYC Metro

“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.”

“Built and deployed a production Llama 3-based RAG document Q&A system using FAISS, addressing context-window limits through chunking and keeping retrieval accurate by regularly refreshing embeddings. Has hands-on orchestration experience with LangChain and LlamaIndex for multi-step LLM workflows (including memory management) and collaborates with non-technical teams (e.g., marketing) to deliver AI solutions like recommendation systems.”

Gabriel Fagundes

Screened

Mid-level AI/ML & Backend Engineer specializing in AI platforms and computer vision

New York, New York6y exp

“Backend engineer with hands-on experience building real-time, low-latency systems: owned the Python backend for a real-time crowd-monitoring product (top 5% at HackHarvard 2025) using OpenCV, GPU YOLO inference (PyTorch), WebRTC, and OAuth. Also has production Kubernetes/GitOps experience (Helm/Kustomize, GitHub Actions, Argo CD), Kafka-based event pipelines, and executed a minimal-downtime on-prem PostgreSQL migration to AWS EC2.”

Butchi Venkatesh Adari

Screened

Mid-level Machine Learning Engineer specializing in LLM platforms and robotic perception

NewYork, NY4y exp

“Built and shipped a production multi-agent personal financial assistant at AlphevaAI on AWS ECS, combining FastAPI microservices, Redis/SQS orchestration, and Pinecone-based hybrid RAG (semantic + BM25) to ground financial guidance. Improved routing accuracy with an embedding-based SetFit + logistic regression intent classifier feeding an LLM router, and optimized UX with live streaming plus cost controls via model tiering and caching.”

“Built and deployed a production LLM/RAG clinical document understanding and summarization system for healthcare, focused on reducing manual review time while meeting strict accuracy, latency, and compliance needs. Demonstrates strong MLOps/orchestration depth (Airflow, Kubernetes, Azure ML Pipelines) and a rigorous approach to hallucination mitigation through layered, source-grounded safeguards and stakeholder-driven requirements with physicians/compliance teams.”

“Built and owned production GenAI systems for both infrastructure automation and customer support. Most notably, they created a self-healing multi-cloud incident response system that automated 65% of tier-1 alerts and reduced application crashes by 75%, and also shipped a hybrid RAG support triage agent that automated 60% of tier-1 inquiries with human escalation guardrails.”

Sachin Kulkarni

Screened

Mid-level AI/ML Engineer specializing in LLMs, NLP, and AWS MLOps

New York, US3y exp

“Recent master’s graduate in robotics with applied experience across reinforcement learning and ROS 2 autonomy stacks. Built an RL-based drone vertiport traffic controller (PPO) focused on reward design and simulation integration, and has hands-on navigation work in ROS 2 including LiDAR preprocessing, SLAM/path planning, and stabilizing TurtleBot3 wall-following. Also brings deployment experience containerizing robotics nodes and scaling them with Kubernetes on AWS.”

“Senior Data Scientist in consulting who has built production RAG systems for insurance/annuity document search at large scale (100K+ PDF pages), emphasizing grounded answers, guardrails, and low-latency retrieval. Experienced in end-to-end MLOps for LLM apps—monitoring, evaluation sets, drift handling, and safe rollouts—and in orchestrating complex pipelines with Prefect/Airflow and deploying services on Kubernetes.”

“Designed and deployed end-to-end LLM-powered AI agent systems to automate knowledge-intensive workflows across marketing/GTM, recruiting, and support. Brings production reliability rigor (evaluation pipelines, monitoring, testing, A/B experiments) plus orchestration expertise (Airflow, Prefect, custom Python) and a track record of translating non-technical stakeholder goals into working AI solutions (e.g., personalized customer engagement agent at Lara Design).”

“Developed and productionized VL-Mate, a vision-language, LLM-powered assistant aimed at helping visually impaired users understand their surroundings and query internal knowledge. Emphasizes reliability and safety via confidence thresholds, uncertainty-aware fallbacks, hallucination grounding checks, and rigorous offline + user-in-the-loop evaluation, with experience orchestrating multi-step LLM pipelines (LangChain-style and custom Python async) and deploying on containerized infrastructure.”

Krishna K

Screened

Junior Machine Learning Engineer specializing in multimodal systems and LLMs

Jersey City, NJ2y exp

“Built and productionized a domain-specific LLM-powered RAG knowledge assistant at JerseyStem for answering questions over large internal document corpora, owning the full stack from FAISS retrieval and LoRA/QLoRA fine-tuning to AWS autoscaling GPU deployment. Drove measurable gains (28% accuracy lift, 25% latency reduction) and improved reliability through hybrid retrieval, grounded decoding, preference-model reranking, and Airflow-orchestrated pipelines (35% faster runtime), while partnering closely with non-technical stakeholders to define success metrics and ensure adoption.”

“Backend/AI engineer who built production-grade intelligence systems in high-stakes domains including tax/legal document analysis and brain tumor MRI workflows. Stands out for combining LLM/RAG product delivery with strong engineering rigor around retrieval evaluation, grounding, validation, observability, and safe fallbacks—turning impressive demos into systems users could actually trust.”

“Built GynAI, an end-to-end maternal clinical decision support platform for OB/GYN practices and hospitals in North America, combining predictive ML with RAG-based LLM explainability. The candidate emphasizes real production ownership across experimentation, deployment, monitoring, and iteration, with reported impact including fewer delayed interventions in high-risk pregnancies and a 15-20% reduction in false positives.”

“LLM engineer/data analyst who built a production RAG QA assistant over the Jurafsky & Martin NLP textbook to reduce hallucinations and provide explainable, source-grounded answers. Experienced with LangChain/LangGraph orchestration, retrieval optimization (embeddings, vector DBs, caching), and rigorous evaluation/monitoring (Retrieval@K, A/B tests, telemetry/drift). Previously communicated analytics insights to non-technical stakeholders at GS Analytics using Power BI and simplified reporting.”