Vetted Vector Search Professionals

“Software developer with a one-year Philips co-op who has already applied AI-assisted coding in production, not just side projects. Stands out for using multi-agent development setups with task-specific sub-agents and a clear human-led orchestration philosophy focused on context, quality control, and security.”

“Backend-focused engineer who uses AI tools pragmatically for rapid development while staying deeply attentive to concurrency, deadlocks, and system reliability. Built foundational AI retrieval architecture for MastadonTutorAI using ChromaDB and RAG pipelines, and is especially interested in applying multi-agent systems to automate complex backend testing and root-cause analysis.”

“Backend/platform engineer who has owned a Python/FastAPI results API and deployed it on Kubernetes with Helm and GitHub Actions-driven CI/CD. Demonstrates strong production operations mindset across performance tuning, monitoring, safe rollouts/rollbacks, and phased migrations, plus hands-on Kafka streaming experience focused on ordering and idempotency.”

Aditya Rao

Screened

Mid-level Software Engineer specializing in backend, AI, and distributed systems

San Jose, CA5y exp

“Software engineer with 4.5 years of startup experience across programmatic advertising, health tech e-commerce, and automobile diagnostics, plus both bachelor's and master's degrees in CSE. Built an agentic global supply chain platform in a hackathon using a highly structured AI-first workflow, and has hands-on experience designing multi-agent debate systems, rollout safeguards, and observability-driven production fixes.”

Ravi Gairola

Screened

Executive engineering leader specializing in AI platforms and cloud infrastructure

San Francisco, CA15y exp

“Engineering leader and hands-on architect with experience managing three teams spanning product, internal tooling, and ML, while also building AI chat and digital twin systems. Particularly notable for a patented memory architecture used to impersonate users in meetings/chat, plus prior delivery of a consumer digital payments system for Razer under tight deadlines and heavy tech debt.”

Pallavi Thorat

Screened

Mid Software Engineer specializing in backend, cloud, and AI systems

USA, USA3y exp

“Front-end engineer with strong experience building sophisticated, data-heavy financial dashboards for finance analysts and business stakeholders. Stands out for combining React/Next.js architecture, D3.js visualization, and browser-level performance tuning to deliver measurable impact, including ~40% operational efficiency improvement and ~35% lower session drop-off.”

“Backend engineer with experience evolving a real-time transaction and rewards processing platform from a tightly coupled architecture into domain-based microservices. Uses REST plus Kafka for synchronous vs. asynchronous workflows, and builds Python/FastAPI APIs with Pydantic contracts, Docker/Kubernetes deployments, and JWT/OAuth-based security; has also supported analytics/dashboard use cases (Power BI).”

“LLM/RAG engineer with production deployments in highly regulated domains (Frost Bank and GE Healthcare). Built secure, explainable document-grounded Q&A systems using LoRA fine-tuning, strict RAG with confidence thresholds, and citation-based responses; also established evaluation/monitoring (golden QA sets, hallucination tracking, drift) and achieved ~40% latency reduction through retrieval/prompt tuning.”

“LLM/RAG engineer who took a PDF-heavy agent from prototype to production for an Africa-based client, combining Pinecone retrieval with robust PDF parsing (unstructured.io, OCR, structured table extraction). Demonstrates strong production mindset (eval metrics, prompt hardening, security/scalability) and measurable optimization impact (30% efficiency gain, 2x faster responses), and has helped close deals by building security-focused POCs for skeptical IT stakeholders.”

“Open-source JavaScript contributor focused on data visualization, extending Chart.js/React with custom plugins for real-time streaming dashboards. Designed an end-to-end telemetry pipeline using Apache Kafka and Azure Cosmos DB, optimizing partitioning, batching, caching, and client throttling to keep latency low and support thousands of concurrent users. Demonstrates strong ownership in fast-changing environments, including building full-stack AI applications and ingestion/ETL pipelines at Robotics Technologies LLC.”

“Built a production AI-driven contract/document extraction system combining OCR, normalization, and LLM schema-guided extraction, orchestrated with PySpark and Azure Data Factory and loaded into PostgreSQL for analytics. Emphasizes reliability at scale—using strict JSON schemas, confidence scoring, targeted retries, and multi-layer validation to control hallucinations while processing thousands of PDFs per hour—and partners closely with non-technical business teams to refine fields and deliver usable dashboards.”

“Built and shipped a production LLM-powered internal operations automation platform using LangChain RAG (Pinecone) and FastAPI microservices, deployed on AWS EKS, serving 10k+ daily interactions. Implemented a rigorous evaluation/observability stack (golden datasets, prompt regression tests, MLflow, retrieval metrics, hallucination monitoring) that drove hallucinations below 2% and improved reliability, and partnered closely with non-technical ops leaders to cut manual lookup work by 60%+.”

“Built a production internal LLM-powered knowledge assistant using a RAG architecture (Python, LLM APIs, cloud services) that answers employee questions with sourced, grounded responses from internal documents. Demonstrates strong practical depth in retrieval tuning (chunking/metadata filters), orchestration with LangChain, and production reliability practices (latency optimization, automated embedding refresh, evaluation metrics, logging/monitoring) while partnering closely with non-technical operations teams.”

Jaideep bommidi

Screened

Senior ML Engineer & Data Scientist specializing in LLM agents, retrieval/ranking, and MLOps

Denton, TX8y exp

“Machine Learning Engineer currently at Webster Bank building an enterprise-scale LLM agent for Temenos Journey Manager/Maestro, using RAG-style multi-stage retrieval with FAISS/Pinecone, hybrid dense+sparse search, and LoRA fine-tuning optimized via NDCG/MAP and A/B testing. Previously handled messy incident/telemetry data at Deuta Werke GmbH with deterministic + fuzzy entity resolution, and has strong production data engineering experience across Spark/Hadoop and Python ETL systems.”

Preetham Reddy Konuganti

Screened

Junior Full-Stack Engineer specializing in AI applications and scalable web platforms

San Jose, CA2y exp

“Full-stack engineer with customer-facing delivery experience who built and deployed a multi-platform social media automation product (Next.js/Node/MongoDB) and optimized it using BullMQ/Redis background jobs, retries, and rate limiting for reliable posting at scale. Also delivered an AI-powered false-positive analysis service in a cybersecurity context, resolving production pipeline stalls via log-driven debugging, parallelization, caching, and LLM guardrails.”

Akhila Kannegari

Screened

Mid-level AI/ML Engineer specializing in FinTech and retail ML systems

Alabama, USA4y exp

“ML-focused candidate with strong Wells Fargo experience building production fraud systems and internal GenAI tools for fraud analysts. Stands out for measurable impact in fraud detection—raising recall from 71% to 88%—while also demonstrating hands-on depth across streaming infrastructure, MLOps, LLM/RAG implementation, and Python service architecture.”

Naveena Musku

Screened

Mid-level AI/ML Engineer specializing in agentic AI and LLM systems

5y exp

“Backend engineer focused on productionizing LLM systems: built a FastAPI-based RAG and multi-agent automation platform deployed with Docker/Kubernetes, prioritizing safe execution and reduced hallucinations. Experienced in refactoring monolithic ML services with feature-flagged incremental rollouts, and implementing JWT/RBAC plus row-level security (e.g., Supabase) for secure, scalable APIs.”

“AI-focused full-stack product builder from Verizon Applied Research who has shipped internal tools spanning API documentation governance, patent exploration agents, and prompt optimization. Particularly strong at turning unreliable or opaque LLM behavior into structured, trustworthy product workflows that enterprise users can actually adopt.”

“Full-stack/backend-leaning engineer with experience spanning fintech platforms, internal AI/RAG assistants, real-time analytics systems, and a zero-to-one academic web platform. Stands out for combining hands-on backend and infrastructure work with product ownership, team guidance, and measurable impact like cutting troubleshooting lookup time from 30 minutes to under 8 minutes and creating reusable UI components adopted across multiple projects.”

“Built and shipped an internal Financial Insights Assistant for banking analysts, owning the experience from workflow design through React frontend, FastAPI backend, and AI search integration. Particularly strong in making AI products usable and trustworthy by surfacing sources, experts, suggested prompts, and search history to improve confidence and speed.”

Mahima Mannava

Screened

Junior Software Engineer specializing in AI, LLM systems, and full-stack development

San Jose, CA3y exp

“AI/full-stack engineer who built a computer-usage agent end to end, including a split local/cloud architecture that used a vision LLM to drive real Chrome workflows while avoiding bot detection. Stands out for combining product-minded systems design, rigorous evals, and prompt iteration to achieve sub-2-second latency and a 20% reduction in automation failures in an early-stage environment.”

Jaya Rathi

Screened

Senior Software Engineer specializing in distributed systems and cloud platforms

Cupertino, CA15y exp

“Full-stack developer who built a unit billing subscription portal in React, Node.js, TypeScript, and PostgreSQL to track employee billable hours and generate paperless invoices, including BambooHR integration and async workflows with RabbitMQ. Also independently exploring agentic AI through a support desk assistant using RAG, ChromaDB, and LangChain, with a strong focus on grounding, type safety, and measurable retrieval quality.”

“Backend engineer who built an NL-to-SQL system at Target, using a multi-step LLM pipeline with vector-store schema retrieval and SQL validation to safely answer business questions. Strong in production FastAPI systems (async, Pydantic, Docker/Uvicorn, load balancing) and security (OAuth2/JWT, scopes, and database row-level security), with experience migrating Flask apps to FastAPI + PostgreSQL using strangler/feature-flagged canary rollouts.”

“AI/LLM engineer who built a production autonomous GenAI content ecosystem that generates short-form scripts, extracts viral highlights from long-form video, and dubs content into 33+ languages. Focused on making LLM outputs production-safe via schema enforcement, token-to-time alignment, critic-agent verification, and scalable async orchestration—cutting manual workflows by ~90% and saving $200k+ annually.”