Vetted Caching Professionals

“Built internal production LLM tools for engineering and support, including a customer-health assistant and a RAG-based incident explainer grounded in logs, metrics, and deploy data. Stands out for combining strong GenAI safety/evaluation practices with pragmatic backend engineering, delivering measurable impact like a 40% drop in data-help requests and answers in seconds instead of minutes or hours.”

Vamsi Reddy

Screened

Mid-level AI/ML Engineer specializing in healthcare and financial ML systems

Nashville, TN5y exp

“ML/AI engineer with hands-on experience shipping both predictive healthcare models and clinical GenAI assistants into production. They combine strong MLOps depth across Azure and AWS with healthcare-specific safety thinking, including PHI guardrails, retrieval grounding, and production monitoring, and they also built internal Python tooling for fraud ML workflows at Capital One.”

Akhil Nakka

Screened

Mid-level Software Developer specializing in FinTech and cloud-native microservices

Remote, USA4y exp

“Full Stack Engineer in fintech (JPMorgan) who owns products end-to-end across React UIs and Spring Boot/Kafka backends, with a strong track record of shipping quickly while maintaining reliability via testing, monitoring, and feature flags. Has hands-on experience scaling microservices for high-volume transactions and debugging production latency using ELK/CloudWatch, plus built an internal Python/Flask automation tool adopted by backend engineers to speed API validation and debugging.”

“Built production-scale dashboards and real-time data systems across fintech and autonomous driving, with hands-on ownership from React frontend architecture through Python/Kafka/Elasticsearch backend pipelines. At Zoox, led development of an automated safety signal attribution platform that replaced manual engineering calculations and surfaced trends through dashboards.”

Saumya Pailwan

Screened

Junior Software Engineer specializing in applied AI and audio ML

Evanston, IL2y exp

“Engineer with unusually mature experience leading AI-assisted development, including orchestrating multiple coding agents across a data pipeline feature as if managing a small engineering team. Stands out for balancing aggressive adoption of AI tools with disciplined judgment around architecture, security, and merge quality, and for translating that experience into stronger tech leadership.”

“Built and shipped a production LLM feature that converts natural-language search requests into Lucene queries for OpenSearch-backed device event data, improving usability for non-technical users. Brings hands-on experience across the full stack of agentic systems: model training, FastAPI/React integration, Kubernetes deployment on AWS, event-driven orchestration with NATS/Kafka, and production-grade evaluation/observability.”

“Candidate has built multiple zero-to-one AI/full-stack products spanning bioinformatics search, rental marketplace semantic search, and an SDR agent for a hospitality startup. Particularly strong at turning LLM/embedding concepts into usable products with modular workflows, explainable outputs, and production-minded infrastructure.”

“AI/full-stack engineer with hands-on experience shipping LLM-powered operational workflows in claims and Medicaid contexts. They built end-to-end TypeScript/React/Node systems with RAG, structured outputs, evals, and human-in-the-loop controls, and can point to concrete impact including 45% lower triage time, 38% better retrieval accuracy, and major manual-effort reductions.”

Yuchen Shen

Screened

Junior Full-Stack Engineer specializing in AI and graph-based applications

Sunnyvale, CA2y exp

“Front-end engineer with experience at Walmart and Sam's Club building sophisticated browser-based UIs, including an internal AI chat experience that combined chat, document rendering, editing, and visualization in one workflow. Stands out for pairing modular React/TypeScript architecture with hands-on browser performance debugging and Apollo/GraphQL optimization for highly interactive enterprise interfaces.”

“Full-stack product builder with strong GTM and applied AI experience, including end-to-end ownership of a production lead intelligence platform that combined React/TypeScript, Python services, external data enrichment, and LLM orchestration. Notably reduced SDR research time from 15-20 minutes to under 2 minutes per account and also drove an 8% revenue increase at Finding Pi by building a customer segmentation framework from analysis of 45k+ users.”

Ilya Kravchenko

Screened

Executive engineering leader specializing in AI-enabled SaaS and internet-scale platforms

Thousand Oaks, CA20y exp

“Engineering leader with a rare blend of SaaS, robotics, and AI systems experience, most recently leading a 21-engineer org under the CTO. They pair strong people/process leadership with hands-on architecture depth, driving dramatic delivery improvements while also building grounded LLM support tooling for warehouse operations where accuracy directly impacts physical execution and customer outcomes.”

“Software engineer with a recent pivot toward AI engineering, combining traditional full-stack web development with multi-agent AI application work using React, Flask, LangChain, and LangGraph. Has experience improving internal platform integrations, scaling production systems, and building revenue-impacting features such as an order tracking module credited with a 20% revenue increase.”

“Full-stack engineer with healthcare and financial services experience who owned an end-to-end appointment scheduling feature at Norton Healthcare, improving scheduling speed and reliability through Angular/TypeScript, SQL Server, Entity Framework Core, Redis caching, and secure APIs. Also brings enterprise modernization experience from IBM and JPMorgan Chase, including microservices migration, CI/CD, and production observability.”

Aaron Mcanerney

Screened

Mid-level Backend Software Engineer specializing in data platforms and reporting

Pleasanton, CA5y exp

“Built a production Medicare Q&A chatbot using OCR (pytesseract), LangChain chunking, ChromaDB embeddings/retrieval, and an Ollama LLM. Thinks in terms of practical tradeoffs (cost, model selection, static vs frequently-updated corpora) and emphasizes deterministic testing plus observability for reliable LLM workflows; also has experience automating non-technical teams' pain points (e.g., recruiting event integration).”

“Full-stack engineer focused on high-throughput document/financial data platforms, building Angular/React front ends and Spring Boot microservices with Python/Flask services for heavy processing. Experienced in designing non-blocking, asynchronous workflows (Celery/RabbitMQ) and deploying containerized systems to AWS ECS with auto-scaling and CloudWatch monitoring.”

Harshal Sawant

Screened

Senior AI Engineer specializing in LLMs, RAG, and MLOps on multi-cloud

8y exp

“Built and productionized a secure internal RAG-based AI assistant (LangChain/FastAPI/FAISS on GCP), tackling real-world issues like latency, retrieval speed, and hallucinations—delivering 25% faster retrieval and 99.9% uptime. Also implemented scalable, reliable ML retraining orchestration with AWS Step Functions/SageMaker/Lambda and partners closely with compliance analysts to iteratively refine prompts and outputs to meet governance standards.”

“Built and owned end-to-end production Conversational AI and RAG systems at Nike for customer support and product search, with strong evidence of measurable impact. Combines LLM product thinking with solid platform engineering, including evaluation pipelines, monitoring, safety controls, and reusable FastAPI frameworks that improved both user outcomes and team velocity.”