Vetted API Integration Professionals

“Early-career engineer with undergraduate research and hospital internship experience building Python/LLM automation systems, including a Study Planner AI and internal RAG tools for messy legal and clinical data workflows. Stands out for combining web scraping, vector search, and frontend integration to replace manual CSV-heavy processes under tight timelines.”

“AI/backend engineer who has built multiple applied AI systems end-to-end, including an underwriting document intelligence copilot, ambient clinical documentation workflows, and a financial analysis agent. Stands out for combining practical LLM architecture choices with reliability mechanisms like human-in-the-loop review, eval frameworks, and grounded retrieval in production settings.”

“Intern-level full-stack engineer who has built across accessibility tech, ad-tech, healthcare software migration, and consumer analytics projects. Stands out for combining hands-on React/frontend work with backend integration, accessibility-aware routing logic, OAuth debugging, and pragmatic product decisions in highly ambiguous environments.”

Brad Anderson

Screened

Executive product leader specializing in FinTech, SaaS, and digital transformation

Texas, USA23y exp

“Product leader with experience spanning telecom, fintech mortgage, and hospitality, including a major AT&T rebuild of a fragmented digital sales flow and AI-enabled lending/onboarding products. Brings a strong mix of legacy modernization, cross-functional alignment, UX iteration, and human-centered thinking about AI as an amplifier rather than a replacement.”

Mainak Pandit

Screened

Senior Frontend Engineer specializing in React, TypeScript, and product-focused SaaS

Hyderabad, India5y exp

“Frontend-leaning product engineer who operated as an end-to-end owner at Cone, a 5-person startup, building the proposal creation and signing platform that became the company’s primary revenue driver. Stands out for combining product judgment with architecture and backend execution, including a self-built Node.js PDF service that cut generation time by ~60% and meaningfully reduced developer overhead.”

“Director-level product leader with experience spanning SaaS platform rebuilds, pricing strategy, and AI-native workflow design. At Skykit, they led a major self-serve/PLG account management transformation across multiple teams, and they also built an AI sales quoting tool using a multi-stage RAG pipeline that reached 100% quote accuracy in user testing.”

Dolly Shyam

Screened

Mid-level Software Engineer specializing in backend systems and AI-driven platforms

New York, NY4y exp

“Backend-focused developer with primary experience in Python, Node.js, databases, and API development. Served as the sole backend engineer on a customer dashboard project, owning database review, API endpoint creation, and coordination with frontend developers for integration.”

Lenny Lin

Screened

Junior Full-Stack Engineer specializing in AI and distributed systems

Champaign, IL3y exp

“Built and owned a hackathon project (Gritto) with a Python/FastAPI backend that routes user text through a sequence of Gemini agents to produce structured JSON outputs. Has hands-on production deployment experience using Docker/Docker Compose, GitHub Actions CI/CD, AWS App Runner, MongoDB, and secrets management (Doppler + migration to AWS Secrets Manager), plus implemented a chat-like experience via multiple HTTP requests when SSE wasn’t viable.”

“Entrepreneur/fintech product builder who raised a $100K pre-seed from ex-Google/Microsoft execs and built a real-time, direct-to-vendor bill pay micropayments platform. Previously helped scale Norton LifeLock to 1M users (2003) and also created Karma LA, a fraud-resistant, verified donation system (including VA veteran verification) aimed at improving trust and conversion in giving.”

“LLM/agentic-systems practitioner who has repeatedly taken LLM-driven pricing/decision services from prototype to production using pilots, guardrails, observability, and staged rollouts. Demonstrates strong real-time incident troubleshooting (dependency timeouts, cached fallbacks) and post-incident hardening (isolation/async/alerts), and also supports go-to-market via developer workshops, technical demos, and sales-aligned POCs.”

“JavaScript engineer and open-source contributor focused on runtime performance, reliability, and developer experience—refactored a widely used client-side API/state library to improve concurrent request handling, error consistency, and UI performance while adding tests and documentation. Also owned improvements to a core microservice at Velsa integrating multiple hospital systems, bringing structure to ambiguous priorities and delivering stability and performance gains from design through deployment.”

“Backend-focused engineer with experience owning a production e-commerce platform end-to-end (TypeScript/Node/Express, React, MongoDB, Redis) including RBAC and contract-based API development. Also worked at Infosys on a large healthcare management system built with Spring Boot microservices, using Kafka for messaging/retries, circuit breakers for resilience, and OpenTelemetry/Swagger for observability and API documentation.”

“Customer-facing technical specialist with experience spanning regulated Class II/III medical devices (FDA compliance, firmware vulnerability escalation) and large-scale industrial/energy deployments. Known for rigorous troubleshooting using logs/network traces and tight documentation/escalation in Salesforce, plus repeatable onboarding and integration practices (API into CI/CD, secrets via env vars, container standardization).”

“Software engineer who shipped an end-to-end internal workforce dashboard at Northwestern Mutual, spanning Spring Boot APIs, PostgreSQL schema/query optimization, and a React + TypeScript UI with role-based access and filtering. Has hands-on production experience deploying via GitHub Actions CI/CD to AWS (Docker, EC2, RDS) and resolving performance incidents by tuning database queries and indexes.”

“Built and deployed a production Llama 3-based RAG document Q&A system using FAISS, addressing context-window limits through chunking and keeping retrieval accurate by regularly refreshing embeddings. Has hands-on orchestration experience with LangChain and LlamaIndex for multi-step LLM workflows (including memory management) and collaborates with non-technical teams (e.g., marketing) to deliver AI solutions like recommendation systems.”

“Consulting-style LLM practitioner who builds enterprise knowledge assistants using RAG and takes them from prototype to production with guardrails, evaluation, and full-stack observability. Experienced partnering with IT and customer-facing teams to demo solutions, build tailored prototypes, and drive adoption through API-based integration.”

“Frontend-focused engineer with enterprise Angular experience integrated with .NET APIs, emphasizing production-ready practices (reusable components, modular architecture, TypeScript standards, Jasmine unit tests, and CI/CD). Has not built Unreal Engine UI systems yet, but articulates how they would translate web UI modularity, separation of concerns, and testing/automation practices to Unreal/CommonUI workflows.”

Aakanksha Desai

Screened

Junior Full-Stack Software Engineer specializing in React, Kubernetes, and AI-powered apps

Scottsdale, Arizona2y exp

“Backend/DevOps-leaning engineer managing multiple customer service platforms end-to-end (requirements through deployment). Built an in-house Python monitoring/alerting solution for Salesforce-to-Java contact sync jobs (Snowflake dependencies) that increased uptime ~60%, and helped modernize delivery by moving the team from manual releases to automated Jenkins-based deployments while coordinating an Oracle EBS→Fusion transition with business/data/IT stakeholders.”

Cameron Shapoorian

Screened

Mid-level Test Automation & AI Integration Engineer

3y exp

“Forward-deployed/solutions-oriented engineer with experience shipping enterprise LLM voice-agent workflows from prototype to production, including variable extraction and API integrations. Demonstrated strong real-time troubleshooting via logs/RCA (e.g., fixing multilingual language-switching by tuning temperature and improving context), and has led technical workshops while partnering with sales/solutions teams to drive customer adoption.”

Uday Dorawala

Screened

Director-level Engineering Leader specializing in SaaS platforms, data, and cloud modernization

Raleigh, NC25y exp

“Former founder with an acquired venture who later led R&D at McClatchy and joined multiple startups. Identified a major client need and built a mobile app platform using ML and AR for home builders, driving adoption by dozens of customers and $1M+ ARR. Strong hands-on builder who can architect MVPs, iterate quickly with A/B testing and user feedback, and scale early engineering teams and culture.”

Ranxin Li

Screened

Mid-Level AI/Full-Stack Engineer specializing in agentic LLM systems and RAG

San Jose, USA2y exp

“Built and deployed Clyra.AI, an AI-driven daily scheduling product that uses a LangGraph-based multi-agent LLM pipeline (task extraction, verification, reflection) grounded with strict RAG over emails/documents/calendars and real-world signals like health metrics. Designed a custom agent orchestrator with bounded loops/termination conditions and a self-auditing verification/reflection layer to reduce hallucinations while controlling latency and cost via caching and model distillation.”

Nikhil Chagi

Screened

Intern Data Analyst specializing in data pipelines and LLM/RAG applications

San Francisco, CA1y exp

“Built and deployed LLM-powered analytics and reporting systems, including a RAG-based assistant over Snowflake that let business users ask questions in plain English instead of writing SQL. Experienced orchestrating LLM agents (LangChain) and serverless reporting pipelines (AWS Lambda/S3/RDS), with a strong focus on grounded outputs, monitoring/evaluation, and data quality—used daily by non-technical finance and operations teams at Cigna.”

“Data/ML engineer with end-to-end ownership of donor-data deployments for a university foundation, delivering major performance and data-quality gains (500K+ records; 24h to 6h processing; duplicates 5% to 1%). Has put an LLM-assisted enrichment workflow into production with retrieval-grounded business rules, versioned outputs for traceability, and strong operational rigor around validation, logging, and CI/CD.”