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Vetted Machine Learning Engineers in the Greater Boston
Pre-screened and vetted in the Greater Boston.
Mid-level Machine Learning Engineer specializing in MLOps, NLP, and data security
Mid-level Data Scientist specializing in risk modeling and machine learning
Mid-level Robotics & Automation Engineer specializing in motion planning and machine vision
Mid-level AI Software Engineer specializing in FinTech and LLM systems
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.”
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 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.”
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 Data Scientist / ML Engineer specializing in applied ML, MLOps, and LLM agents
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
Akshay Bharadwaj Kunigal Harish
Mid-level Machine Learning Engineer specializing in NLP, computer vision, and LLM systems
“Built a production multi-agent cybersecurity defense simulator orchestrated with CrewAI, combining Red/Blue team LLM agents, a RAG runbook retriever, and an RL remediation agent trained via state-space simplification and reward shaping for rapid incident response. Also partnered with quant analysts and fund managers to deliver an automated trading and portfolio management system using statistical methods plus CNN/LSTM models, reporting up to 15% weekly ROI.”
Jaykumar Kotiya
Mid-level Machine Learning & AI Engineer specializing in Generative AI, NLP, and MLOps
“Built and deployed production LLM systems for summarizing sensitive legal and financial documents, emphasizing GDPR-aligned privacy controls and scalable hybrid cloud architecture. Experienced with Kubernetes/Airflow orchestration and rigorous testing/monitoring practices, and has delivered measurable business impact (18% conversion lift) by translating AI outputs for non-technical marketing stakeholders.”
Hari Krishna Kona
Mid-level AI/ML Engineer specializing in Generative AI and LLM-powered NLP
“LLM/AI engineer who built a production automated document-understanding pipeline on Azure using a grounded RAG layer, designed to reduce manual review time for unstructured financial documents. Demonstrates strong real-world scaling and reliability practices (Service Bus queueing, Kubernetes autoscaling, observability, retries/circuit breakers) plus rigorous evaluation (shadow testing, replaying traffic, multilingual edge-case suites) and stakeholder-friendly, evidence-based explainability.”
Vikram Sandigaru
Mid-level AI Engineer specializing in AI agents, RAG pipelines, and LLM evaluation
“Built and shipped production LLM systems at Founderbay, including a low-latency voice agent and a graph-based multi-agent research assistant. Strong focus on reliability in real workflows—hybrid SERP + full-site scraping RAG, grounding guardrails, validation checkpoints, and transcript-driven evaluation—plus performance tuning with async FastAPI, Redis caching, and containerization. Also partnered with a non-technical ops lead to automate post-call follow-ups via call summarization, field extraction, and tool-triggered actions.”
Keeravani Chekuri
Mid-level AI/ML Engineer specializing in LLM systems and MLOps
“Built and deployed an AI tutoring assistant end-to-end at Nexora School, spanning discovery with school districts, multi-agent LangGraph/RAG architecture, AWS Bedrock migration, and post-launch stabilization. Stands out for combining hands-on LLM systems engineering with strong educator-facing trust building, FERPA-driven architecture decisions, and disciplined production practices around evals, logging, and messy document ingestion.”
Mid-level ML Engineer specializing in LLMs, document AI, and computer vision
Mid-level AI/ML Engineer specializing in GenAI, RAG, and multi-agent LLM systems