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Vetted AI & Machine Learning Professionals in the Greater Boston
Pre-screened and vetted in the Greater Boston.
Popular Titles
Mid-level Machine Learning Engineer specializing in MLOps, NLP, and data security
Intern Computer Vision/Robotics Engineer specializing in perception and tracking
Junior AI/ML Engineer specializing in NLP, generative AI, and multimodal modeling
Mid-level AI/ML Engineer specializing in LLMs, RAG pipelines, and cloud-native MLOps
Mid-level AI/ML Engineer specializing in NLP, MLOps, and fraud & risk analytics
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and MLOps
Anju Vilashni Nandhakumar
Entry-level Machine Learning Engineer specializing in RAG and NLP systems
“Built a 24/7 Python/LangChain email agent in production with validation, circuit breakers, human-review escalation, and structured observability. Also applied data and automation skills at Community Dreams Foundation, including turning a vague donor-insights request into a usable donor-risk prediction workflow and raising ETL reliability from roughly 85% to 99% by diagnosing SQLite concurrency issues.”
Sai Bandaru
Mid-level Machine Learning Engineer specializing in fraud detection and LLM systems
“At FiVerity, built and deployed a production LLM/RAG-based Information Gathering Tool for credit union fraud analysts that generates auditable investigation summaries from verified evidence. Focused on high-stakes constraints—hallucination prevention, cross-entity leakage controls, compliance/PII-safe monitoring, and latency—while also shipping customer-facing agentic workflows using CrewAI and LangGraph in close partnership with fraud and compliance stakeholders.”
Darshan Rahul Rajopadhye
Junior AI/ML Engineer specializing in LLM agents and RAG systems
“Backend/data engineer who built a production-ready multi-agent financial intelligence system (Mycroft) that orchestrates specialized AI agents to analyze real-time market data using FastAPI and Pinecone vector search. Brings strong security/reliability instincts (rate limiting, JWT/OAuth2, retries/backoff, health checks) and has caught high-impact data integrity issues in financial migrations (timezone normalization across global legacy systems).”
Chaitanya Kalagara
Mid-level Machine Learning Engineer specializing in LLMs, GenAI, and Computer Vision
“LLM/agent engineer who built a production multi-agent research automation system using LangGraph (planner, retriever with FAISS, supervisor, evaluator) with structured outputs and citation tracking for traceable reports. Emphasizes reliability and operations—LangSmith-based observability, multi-level testing, hallucination mitigation, and latency/cost controls—plus prior experience as a Computer Vision Software Engineer at Deepsight AI Labs working directly with non-technical customers.”
Yash Pankhania
Mid-level AI Engineer specializing in LLMs, RAG, and data engineering
“AI Engineer Co-Op at Northeastern University who built a production Patient Persona Chat Bot to help nursing students practice clinical interactions, fine-tuning Llama 3 and integrating a LangChain + Pinecone RAG pipeline deployed on Amazon Bedrock. Emphasizes clinical accuracy and reliability with guardrails, retrieval filtering, and continuous evaluation, and also brings strong data engineering/orchestration experience (Airflow, EMR/PySpark, ADF, dbt, Databricks, Snowflake).”
Yash Namdeo Nikhare
Mid-level Machine Learning Engineer specializing in data security and GenAI systems
“Built Hexagon’s production Text-to-CAD Copilot that converts text and rough sketches into editable CAD code, combining GraphRAG (Neo4j/LangChain) with a Gemini-powered vision module and multi-agent geometric validation—cutting manual modeling from a day to ~45 seconds and driving retrieval latency below 50ms. Also has large-scale GCP data/ML orchestration experience (Airflow/Cloud Composer, Dataflow, Pub/Sub, Snowflake) processing 50M+ daily records with drift monitoring and automated reliability controls.”
Sabita Kumari
Senior Full-Stack AI Engineer specializing in LLM/RAG agentic systems
“Built and deployed JobMatcher AI, an LLM-driven workflow automation product for job seekers that extracts requirements from job descriptions, matches to user skills, and generates tailored outreach. Demonstrated strong production engineering by cutting per-run cost ~70%, improving reliability with retries/backoff/fallbacks, and reducing hallucinations via schema validation and templating; also orchestrated the system with LangGraph plus Docker Compose across API, vector DB, and workers.”
Mid-level AI/ML Engineer specializing in healthcare ML systems and cloud MLOps
Mid-level AI/ML Engineer specializing in agentic AI and RAG systems
Mid-level Applied Scientist specializing in LLMs, multimodal AI, and MLOps
Mid-level Machine Learning Engineer specializing in fraud detection, NLP, and MLOps
Mid-level AI/ML Engineer specializing in healthcare and generative AI
Senior Machine Learning Software Engineer specializing in Azure enterprise AI
Dhruv Kamalesh Kumar
Mid-level Generative AI Engineer specializing in LLM agents and RAG applications
“GenAI builder and technical lead with ~2 years of hands-on production experience, including GENIE (a GenAI sandbox for ~44,000 Massachusetts public-sector employees) and A-IEP, a multilingual platform helping parents understand complex IEP documents (cut processing from ~15 minutes to ~2 and used by 1,000+ parents). Strong in RAG/agentic architectures, AWS serverless + Step Functions orchestration, and rigorous evaluation/guardrails for reliable real-world deployments.”
Ragamalika Karumuri
Mid-level AI Engineer specializing in LLM apps, RAG pipelines, and multi-agent systems
“AI Engineer at Humanitarian AI who has built and productionized both a LangGraph-based multi-agent workflow system and a RAG pipeline (OpenAI embeddings + vector DB) with rigorous evaluation/guardrails. Reports strong measurable impact (60% faster workflow delivery, 40% fewer incidents, 70% reduced research time) and has prior enterprise modernization experience at Infosys migrating ETL to microservices with zero production incidents.”