Vetted AI & Machine Learning Professionals in New York

Yukta Chikate

Screened

Mid-level Machine Learning Engineer specializing in safety-critical and uncertainty-aware ML systems

Brooklyn, NY5y exp

“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.”

“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.”

Archana yaramala

Screened

Mid-level AI/ML Engineer specializing in deep learning, MLOps, and LLM applications

NY, USA4y exp

“Built and deployed production LLM assistants for internal Q&A and customer-feedback summarization, emphasizing reliability (RAG, prompt tuning, validation/whitelisting) and privacy safeguards. Improved adoption by adding explainable outputs and a user feedback mechanism, and has hands-on orchestration experience with Aflow and Azure Logic Apps.”

“IBM engineer who built and deployed a production RAG-based LLM assistant using LangChain/FAISS with a fine-tuned LLaMA model, served via FastAPI microservices on Kubernetes, achieving 99%+ uptime. Demonstrates strong practical expertise in reducing hallucinations (semantic chunking + metadata-driven retrieval) and managing latency, plus mature MLOps practices (Airflow/dbt pipelines, MLflow tracking, monitoring, A/B and shadow deployments) and effective collaboration with non-technical stakeholders.”

“ML/AI engineer with hands-on experience building production MLOps systems for predictive maintenance and demand forecasting, including deployment, monitoring, and iterative retraining. Also shipped a RAG-based employee onboarding chatbot integrated with ServiceNow APIs and reports business impact of roughly $300k/month in reduced stockout and overstock costs.”

“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).”

“Built production AI agents that convert natural-language requests into structured workflows using LangChain, tool calling, and a Kafka/Kubernetes backend, with strong emphasis on tracing, validation, and self-correcting failure handling. Also drove a zero-to-one Research Day judging platform spanning React, Flask, RAG, and ILP-based assignment optimization for ~100 live posters, achieving 99% uptime and winning Best Web App.”

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.”