.png){kind=logo}
Vetted AI & Machine Learning Professionals in the NYC Metro
Pre-screened and vetted in the NYC Metro.
Staff GenAI Solutions Architect specializing in LLMs, RAG, and AI platforms
Mid-level Full-Stack AI Engineer specializing in agentic SaaS and LLM systems
Adithya Gururaj
Junior AI Engineer specializing in agentic AI, RAG, and voice/telephony systems
“LLM/agent engineer who has built production multi-agent systems (LangChain/LangGraph) for enterprise workflows like email and calendar automation, with a strong focus on latency, tool-calling accuracy, and evaluation via LangSmith. Also worked on AI long-term memory using knowledge graphs at VEAI and communicated the approach and tradeoffs to CEO/CTO stakeholders.”
David Gil
Senior Technical Artist specializing in game art pipelines, automation, and localization
“Marketing creative/production specialist with experience localizing Facebook ads, marketing emails, and animated billboards for regional markets at Hi5 Games and Tiffany & Co. Has built permutation-based ad templates in Figma for crypto/DeFi marketing (CheatDAO/QiDAO) and produces lightweight motion assets using After Effects/Premiere with optimized encoding workflows.”
Tarun Gowda
Mid-level AI Engineer specializing in Generative AI and multimodal RAG
“Full-stack engineer who helped build and launch an internal genAI platform called GAIL, supporting multiple LLMs, confidential document upload for RAG pipelines, and collaborative chat. Worked across FastAPI, React/TypeScript, AWS/DynamoDB, and Azure, with notable ownership of backend RAG logic, MCP integration architecture, and frontend fixes that improved chat usability.”
Mid-level Machine Learning Engineer specializing in healthcare and financial AI
Lavan Gajula
Mid-level GenAI Engineer specializing in LLM agents and production AI workflows
“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).”
Sonam Chhatani
Mid-level AI Engineer specializing in causal inference and LLM research
“LLM engineer who has deployed a production system combining LLMs with causal inference (DoWhy) to enable counterfactual “what-if” analysis for experimental research, including a robust variable-mapping/validation layer to reduce hallucinations. Also partnered with non-technical operations leadership at Irriion Technologies to deliver an AI-assisted onboarding workflow that cut onboarding time by 50% and reduced manual errors by ~40%.”
Phani Tarun Munukuntla
Junior Machine Learning Engineer specializing in LLMs, NLP, and MLOps
“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
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 Full-Stack & LLM Application Developer specializing in agentic RAG systems
Mid-level Sonic Arts & Multimedia Editor specializing in audio/video production and design
Mid-level AI/ML Engineer specializing in LLMs, RAG, and AI agents
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 Backend Engineer specializing in Java, Spring Boot, AWS, and scalable systems
Mid-level AI/ML Engineer specializing in LLMs, RAG, and agentic AI systems
Mid-level AI Engineer specializing in LLM automation and RAG systems
Mid-level Full-Stack Engineer specializing in AI products and FinTech
Priyank Jhaveri
Junior AI/ML & Mobile Engineer specializing in LLMs, synthetic data, and React Native
“Currently at Uplift AI shipping production LLM features that generate personalized growth insights from user reflections using BERT + embeddings + RAG, with strong safety/guardrail practices for sensitive contexts. Also built an end-to-end React Native UGC challenge submission/moderation system that improved repeat submissions and 7-day retention, and has applied rigorous clinical-style evaluation methods on a dental X-ray disease detection project to reduce false negatives.”
“Built a production ad-spend optimization system that combined deterministic audit logic with LLM-generated explanations, surfacing severe inefficiencies including 70-90% wasted spend in some Google Ads accounts. Stands out for pairing measurable business impact with pragmatic AI safety and usability decisions, including approval-gated execution and structured, human-readable recommendations.”