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Vetted Machine Learning Engineers in the Bay Area
Pre-screened and vetted in the Bay Area.
Manvi Panjwani
Mid-level Machine Learning Engineer specializing in cloud, governance automation, and distributed systems
“Governance engineer intern at GSK who built policy-as-code automation using Open Policy Agent/Rego integrated into GitHub CI/CD and Terraform workflows. Also built and shipped a voice-enabled expense tracking app using speech-to-text + LLM structured extraction with strong validation, retries, and semantic guardrails, and designed the supporting PostgreSQL data model with performance-focused indexing.”
Vidit Naik
Junior AI/ML & Full-Stack Engineer specializing in LLMs and RAG systems
“Forward-deployed engineer who built a production AI drone-control chatbot that lets users fly a drone via natural language while viewing a real-time feed. Implemented RAG over drone SDK documentation (vector DB + top-k retrieval) and LoRA fine-tuning, with a focus on latency, token efficiency, and cost reduction, and regularly works with non-technical clients to integrate and explain AI system architecture.”
Farida Poor
Junior Machine Learning Engineer specializing in NLP and multimodal transformers
“Built and deployed LLM-powered agentic chatbot and text-to-SQL systems using LangGraph/LangChain (and Bedrock), structuring workflows as DAGs with planning/replanning and validation to improve tool-calling reliability and reduce hallucinations. Operates production feedback loops with online/offline metrics, drift detection, and LangSmith-based evaluation pipelines, and regularly partners with business stakeholders and clinicians using slide decks and visual charts.”
Lakshmi Priya Ramisetty
Mid-level ML & Data Engineer specializing in GenAI, graph modeling, and fraud/risk analytics
“Built a production AI fraud/risk scoring platform at BlueArc that ingests web business/product/site data, generates text+image embeddings, and connects entities in a graph to detect reuse patterns and links to known bad actors. Optimized for scale with incremental graph re-scoring and delivered investigator-friendly explainability by surfacing the exact signals/relationships behind each score; orchestrated workflows with Airflow and GCP event-driven components (Pub/Sub, Dataflow, Cloud Run) and has recent LLM workflow orchestration experience (retrieval, prompting, scoring).”
Mid-level Machine Learning Engineer specializing in LLMs, semantic search, and distributed data pipelines
Mid-Level Software Engineer specializing in Full-Stack and Generative AI
Mid-level AI/ML Engineer specializing in risk scoring, forecasting, and real-time fraud detection
Junior Software Engineer specializing in ML, backend systems, and LLM applications
Mid-level AI/ML Engineer specializing in LLM systems and MLOps
Mid-level Machine Learning Engineer specializing in GenAI and cloud-native ML systems
Mid-level Full-Stack Software Engineer specializing in backend systems and applied ML/LLMs
Director-level AI/ML Software Engineer specializing in production LLM and healthcare systems
Mid-level Machine Learning Engineer specializing in LLM chatbots and RAG systems
Mid-level Software Engineer specializing in Generative AI and ML platforms
Mid-level Machine Learning Engineer specializing in multimodal content moderation and MLOps
Junior Machine Learning Engineer specializing in LLM infrastructure and inference optimization
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and cloud deployment
Mid-Level Software Engineer specializing in ML and healthcare data systems
Drashti Magia
Junior AI Engineer specializing in LLMs, multimodal ML, and applied machine learning
“Software engineer with a disciplined, production-minded approach to AI-driven development: uses ChatGPT, Claude, GitHub Copilot, and scoped coding agents to accelerate delivery without giving up architectural judgment. Notably applied a multi-agent workflow on ClinicOps Copilot, using agents for planning, Bedrock/RAG scaffolding, and failure testing while personally owning architecture, grounding quality, and end-to-end review.”
Divya Narahari
Mid-level Software Engineer specializing in AI/ML systems and backend platforms
“New grad focused on AI systems and agent-based development, with hands-on experience using LLMs as a coding partner and building RAG-based document processing workflows. Stands out for practical experimentation with semantic chunking, retrieval optimization, and multi-agent architectures, including redesigning a RAG workflow by adding a reasoning agent to improve response accuracy and reliability.”
Harshal J Hirpara
Mid-level Machine Learning Engineer specializing in LLM alignment and applied reinforcement learning
“AI/LLM engineer who has shipped production systems end-to-end, including a note-taking product (Notey) combining audio/image capture, ASR, summarization, and a semantic chat agent over past notes. Also has applied ML experience in healthcare, collaborating directly with doctors to validate an EEG seizure-detection pipeline, and uses Kubernetes to optimize GPU usage for LLM training.”