Vetted Vector Databases Professionals

Sumer Joshi

Screened ReferencesStrong rec.

Senior Full-Stack Engineer specializing in backend systems and AI applications

Remote13y exp

“Candidate is deeply focused on AI-native software development, using a deliberate planner/implementer agent workflow with tools like Cursor, Claude, and Kimi. They also built a personal project called Config Proctor, an AI-agent-driven Terraform/AWS self-healing system that identifies infrastructure configuration gaps and proposes fixes.”

“Senior engineering leader with deep hands-on experience building and scaling high-throughput platform infrastructure, including a Qualys core data platform using Kafka, Cassandra, OpenSearch, Ceph, and API gateways. He combines architecture depth with people leadership, and has led complex cross-team initiatives such as rolling out a Kubernetes-based container platform across 20 application teams using a phased migration strategy.”

“Robotics-focused engineer with ROS 2 experience who has built and debugged real-time, distributed control/orchestration systems under production-like latency and safety constraints. Led platform changes at Persona for a real-time verification orchestration system using deterministic state machines and async workers, and has hands-on experience stabilizing multi-robot navigation/SLAM behavior using rosbag, RViz, and stress testing in simulation (Gazebo).”

Yuan-Hsuan Wen

Screened

Intern Software Engineer specializing in AI agents, RAG pipelines, and semiconductor systems

Taipei, Taiwan3y exp

“Built a web-based interface that connects an internal bug system to an LLM for initial debugging and issue classification, aiming to boost QA and software engineer efficiency while balancing latency and accuracy. Worked as a one-person project and managed constraints like limited hardware and difficulty extracting team debugging context, relying on manager communication and rapid modeling to validate direction.”

Christopher Bun

Screened

Executive AI/ML technology leader specializing in healthcare, biotech, and legal AI

Irvine, CA17y exp

“Repeat founder and startup advisor with experience spanning academic, health tech, legal tech, sports, and gaming. Has participated in fundraising and due diligence and has built companies, engineering teams, and software platforms from scratch, with a strong product-design-first approach to product-market fit and market selection.”

Arun Ganesh

Screened

Junior Software Engineer specializing in healthcare AI and cloud infrastructure

San Francisco, CA3y exp

“Amazon Health AI engineer who has owned both full-stack clinical product features and production LLM systems end to end. Built HIPAA-compliant GraphQL and agentic RAG architectures for provider workflows across 125,000+ patients, with measurable impact including 30% higher clinical relevance, 55% lower lookup time, and 12% less false medical information.”

Shailaja Domala

Screened

Executive product and AI leader specializing in data platforms and analytics

California, USA24y exp

“Engineering leader with deep experience at Visa building and modernizing large-scale analytics platforms, including refactoring legacy systems into globally available microservices on AWS. Combines hands-on technical judgment in architecture, search platform evaluation, and service reliability with management of distributed international engineering and data teams.”

“Machine learning/NLP practitioner at J.P. Morgan who led development of a production RAG system and an entity resolution pipeline for complex financial data. Deep hands-on experience with embeddings (Sentence-BERT), vector search (FAISS/pgvector), LLM fine-tuning (LoRA/PEFT), and rigorous evaluation (human-in-the-loop + A/B testing) backed by strong MLOps on AWS (Docker/Kubernetes, MLflow, Prometheus/Datadog).”

“Built and productionized an AWS-hosted, Kubernetes-orchestrated RAG assistant that enables natural-language Q&A over internal document repositories with grounded answers and citations. Demonstrates strong applied LLM engineering: hallucination mitigation, hybrid retrieval + re-ranking, and rigorous evaluation via benchmarks and A/B testing, plus real-world scaling of compute-heavy inference with dynamic batching and monitoring.”

Nishitha Thummala

Screened

Mid-level AI/ML Engineer specializing in LLMs, RAG, and scalable inference

San Francisco, CA6y exp

“Backend/retrieval-focused engineer with production experience at Perplexity building a large-scale real-time Q&A system using retrieval-augmented generation, emphasizing low-latency, high-quality answers through ranking, context optimization, and caching. Also has orchestration experience from both product-facing LLM pipelines and large-scale infrastructure workflows at Meta, and has partnered with non-technical stakeholders to align AI trade-offs with business goals.”

“CTO-minded builder seeking to join a startup; previously created an AI-driven platform that abstracted away DevOps and infrastructure for drug discovery researchers. Emphasizes high-leverage, zero-to-one execution with managed cloud/open-source tooling, and a strong reliability/reproducibility mindset validated against existing scientific pipelines.”