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Vetted Machine Learning Engineers in the NYC Metro
Pre-screened and vetted in the NYC Metro.
Krishna K
Junior Machine Learning Engineer specializing in multimodal systems and LLMs
“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.”
Junior AI & Machine Learning Engineer specializing in LLM automation and RAG systems
Junior AI Engineer specializing in distributed AI and RAG systems
Mid-level AI/ML Engineer specializing in LLM, RAG, and semantic search systems
Mid-level AI/ML Engineer specializing in LLMs, RAG, and agentic AI systems
hamza Khan
Mid-level DevOps & Platform Engineer specializing in AI/ML infrastructure
“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.”
Bill Zoheb
Senior AI Engineer specializing in LLMs, RAG, and production ML systems
“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.”
Venkatalakshmi Kottapalli
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and MLOps
“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.”
Abhiram Mullapudi
Mid-level ML Engineer specializing in real-time inference and anomaly detection
“Built DocMind, an end-to-end PDF chat assistant using React/TypeScript, FastAPI, and Postgres/pgvector, showing full-stack ownership plus practical performance tuning and AWS debugging skills. At Social Tech Labs, improved onboarding, shipped lean under ambiguity, and created a reusable low-latency feature serving layer that reduced duplicated infrastructure work across models.”
Taruni Reddy Ampojwala
Mid-level GenAI Engineer specializing in LLM agents and RAG systems
“Built and deployed a production RAG-based LLM assistant that answers day-to-day operational questions from internal PDFs/SOPs, with strong emphasis on data consistency (metadata versioning, confidence thresholds, conflict handling) and low-latency retrieval at scale. Experienced designing and orchestrating multi-agent LLM workflows (retrieval/validation/generation) and pipeline orchestration for ingestion/embedding/vector-store updates, plus iterative delivery with non-technical operations/business stakeholders.”
Aishwarya Yogananda
Entry-level Machine Learning Software Engineer specializing in backend AI systems
“Built an AI GTM Copilot that consolidated 5+ sales tools into a single browser-based workflow for sales teams, combining LLMs, data enrichment, and outreach automation. Brings a rare blend of ML, backend, and front-end experience, with concrete wins in performance optimization, workflow design, and measurable UX improvements.”
Mid-level Generative AI Engineer specializing in LLMs and RAG for enterprise and FinTech
Mid-level Machine Learning Engineer specializing in GenAI, RAG, and medical imaging
Mid-level AI/ML Engineer specializing in Generative AI, RAG agents, and multimodal systems
Mid-level Generative AI/ML Engineer specializing in LLMs, RAG, and MLOps
Vishesh Patel
Junior AI/ML Engineer specializing in Python ML, NLP, and model deployment
“Built and productionized a real-time social-media sentiment analysis system used by a marketing team to monitor brand/campaign performance. Experienced in orchestrating LLM workflows with LangChain (validation → prompting → parsing → post-processing), plus monitoring, retraining, and RAG-style retrieval using embeddings/vector stores to keep outputs reliable over time.”
Jeet Choksi
Mid-level Machine Learning Engineer specializing in real-time AI and data platforms
“ML/NLP engineer who has built production systems end-to-end: a real-time recommendation platform (100k+ profiles) using BERTopic-style clustering and a RAG-based news summarization/recommendation stack with ChromaDB. Strong focus on scaling and reliability (GPU batching, Redis caching, Kafka ingestion, Docker/Kubernetes, Prometheus/Grafana) and on maintaining model quality over time via drift monitoring and retraining triggers.”
Mid-level GenAI Engineer specializing in AI agents and FinTech platforms