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Vetted Machine Learning Engineers in the Bay Area
Pre-screened and vetted in the Bay Area.
Venkatesh Nagubandi
Mid-level Software Engineer specializing in ML, LLM apps, and cloud data systems
“Built a production SQL chatbot for access-log analytics that replaced manual custom report requests with natural-language querying, using LangGraph and a ChromaDB-backed RAG pipeline for grounded, consistent answers. Implemented a privacy-preserving design where the LLM never sees raw customer data (only query metadata) and has experience building multi-agent/tool-calling systems with LangGraph (DeepAgents), including solving sub-agent communication drift via self-reflection.”
Likith Kumar Tarala
Mid-level Machine Learning Engineer specializing in deep learning and generative AI
“AI/ML engineer who has deployed transformer-based NLP systems to production via Python REST APIs and Kubernetes on AWS/Azure, with a strong focus on latency optimization (p95), reliability, and scalable orchestration. Demonstrates pragmatic model tradeoff decision-making and strong stakeholder collaboration—improving adoption by making outputs more actionable with summaries, extracted fields, and confidence indicators.”
Yogita Adari
Mid-level AI Engineer specializing in generative and multimodal systems
“Built and productionized an agentic LLM automation system for an insurance client to determine medication eligibility, using prompt-chaining plus a RAG pipeline over policy rules and deploying on AWS (Lambda/Step Functions, Bedrock) with a serverless architecture. Addressed major data/schema mismatch issues via a semantic matching pipeline and validated performance through human agreement scoring, A/B testing, KPI monitoring, and confidence-based human-in-the-loop review.”
Harsh Tripathi
Mid-level Machine Learning Engineer specializing in LLMs, agentic AI, and risk/fraud modeling
“Built and productionized an agentic LLM workflow during a summer internship to transform unstructured clinical reports into analytics-ready structured data, using a LangChain multi-agent design plus an LLM-as-a-judge layer to control quality in a regulated setting. Also has experience orchestrating ML pipelines at Piramal Capital using AWS Step Functions/EventBridge/CloudWatch, with strong emphasis on observability, evaluation rigor, and measurable impact (80–90% reduction in manual data entry).”
Aishwarya Thorat
Intern Data Scientist specializing in ML engineering and LLM agentic workflows
“Built an agentic, multi-step LLM system that generates full-stack code for API integrations using LangChain orchestration, Pinecone/SentenceBERT RAG, and a human-in-the-loop feedback loop for iterative code refinement. Also collaborated with non-technical content writers and PMs during a Contentstack internship to deliver a Slack-based AI workflow that generates and brand-checks articles with one-click approvals.”
Rong Hu
Junior Robotics Data Engineer specializing in multi-sensor perception datasets
“Robotics software engineer focused on perception data pipelines and multi-robot coordination. Built ROS 2 (rclpy) nodes for synchronized RGB/ToF/pose processing and scaled a perception training data generation pipeline from single-object to multi-object while preserving backward compatibility. Also has strong DevOps experience deploying containerized APIs on Kubernetes with Kustomize and automated releases via GitHub Actions.”
Laasya Muktevi
Intern Machine Learning Engineer specializing in forecasting, NLP, and RAG systems
“Intern who built and deployed a production LLM-powered contract analysis system for finance teams: Azure Document Intelligence for text/table extraction plus Gemini prompting to surface key terms and risks via an async API and simple UI. Emphasizes reliability in production with fallbacks, guardrails against hallucinations, and operational concerns like latency/cost/versioning, delivering summaries in under 30 seconds instead of hours.”
Senior Data Scientist / AI/ML Engineer specializing in NLP, LLMs, and RAG systems
Mid-level AI/ML Engineer specializing in Generative AI and MLOps
Mid-level Machine Learning Engineer specializing in LLMs, NLP, and computer vision
Mid-level AI/ML Engineer & Data Scientist specializing in MLOps, LLMs, and anomaly detection
Mid-level Machine Learning & Generative AI Engineer specializing in healthcare and retail
Mid-level AI Engineer specializing in autonomous agents and AI security
Mid-level Machine Learning Engineer specializing in NLP, time-series forecasting, and edge AI
Mid-level Machine Learning Engineer specializing in Generative AI, NLP, and recommender systems
Rohan Karle Sudarshan
Mid-level Software Engineer specializing in AI, backend systems, and data platforms
“Built and shipped production AI features for Aiden, including a natural-language agent and a Knowledge Hub ingestion/retrieval system. Stands out for hands-on debugging of real LLM production issues across providers like OpenAI and AWS Bedrock, improving reliability and achieving 90% response/retrieval consistency through direct LiteLLM integration, validation, monitoring, and async system design.”
TingYu Chou
Entry-level ML Systems Engineer specializing in LLM infrastructure and recommender systems
“Engineer with a mature, agent-oriented approach to AI-driven software development, using structured planning, TDD, and verification loops rather than ad hoc prompting. Has hands-on experience acting as a tech lead for multiple AI agents in an LLM intelligent routing project, coordinating implementation, testing, debugging, and edge-case review with strong attention to system tradeoffs.”
Venkatarama Sai Teja Dasarathi
Mid-level Machine Learning Engineer specializing in deep learning and generative AI
“ML/NLP engineer with hands-on experience building production systems for unstructured insurance claims and customer data linking. Delivered measurable impact at scale (millions of documents), combining transformer-based NLP, vector search (FAISS/Pinecone), and human-in-the-loop validation, and has strong production workflow/observability practices (Airflow, AWS Batch, Grafana/Prometheus).”