Vetted Unit Testing Professionals

“AI engineer with experience taking a GPT-4-powered GenAI career coach toward production on Azure AI Foundry, re-architecting the backend with hybrid (vector + keyword) search and RAG optimizations to cut latency by 50%. Also has client-facing TCS experience building healthcare ETL pipelines and delivering error-free monthly reports, plus current work analyzing agentic system reasoning traces and guardrail drift as an AI research fellow.”

“Data/ML engineer in financial services (Northern Trust) who built a production RAG-based LLM system to connect structured transaction/portfolio data with unstructured market and internal documents for risk teams. Strong in end-to-end pipelines (AWS Glue/Airflow/PySpark), entity resolution, and taking models from prototype to reliable daily production with performance tuning (LoRA + TensorRT) and monitoring.”

Ashutosh Khatavkar

Screened ReferencesStrong rec.

Mid-level SDET/Software Engineer specializing in test automation and CI/CD

Syracuse, NY3y exp

“AAA game QA professional from Ubisoft (For Honor) with deep live-service multiplayer experience. Known for owning network/competitive integrity risks and building a custom network simulation tool to reliably reproduce desync issues, accelerating debugging and saving 100+ hours. Strong end-to-end QA process skills spanning test planning, triage, regression, and release verification using JIRA/TestRail.”

“Built a 24/7 Python/LangChain email agent in production with validation, circuit breakers, human-review escalation, and structured observability. Also applied data and automation skills at Community Dreams Foundation, including turning a vague donor-insights request into a usable donor-risk prediction workflow and raising ETL reliability from roughly 85% to 99% by diagnosing SQLite concurrency issues.”

“Full-stack engineer who built and scaled an AI-powered web product (React/Next.js + TypeScript/NestJS) with MongoDB, Redis, and RabbitMQ. Strong in rapid iteration while maintaining production quality—uses versioned APIs, feature flags, CI/CD, and observability (correlation IDs/structured logs) to ship frequently and debug distributed workflows. Also created an internal operations dashboard for real-time visibility and control of background jobs/AI workflows that was adopted quickly by ops and product teams.”

Likitha Kukunarapu

Screened References

Mid-level Applied AI Engineer specializing in data engineering and healthcare AI

Remote, USA3y exp

“Built production LLM agents spanning document Q&A, financial insight generation, and ERP-like operational data workflows, with a strong focus on reliability, grounding, and evaluation. Stands out for translating LLM systems into measurable business outcomes, including 70%–80% support workload reduction and a fallback-rate improvement from 18% to 8% through targeted RAG iteration.”

Nicholas Pinero

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in Unity XR/VR training simulations

Remote, USA8y exp

“Unity/C# VR developer who owned a next-gen replay/review system end-to-end, improving determinism so recorded actions (e.g., shots) replayed consistently. Also built a Jenkins-triggered GameDriver-based VR QA automation suite that ran nightly builds and cut manual QA effort by ~75%, and contributed to Photon PUN multiuser mode with hands-on network debugging.”

“QA professional from fintech/SAP security and complex identity systems who has owned end-to-end testing across the SDLC, including being the sole QA on a high-risk payment platform carrier migration. Demonstrated strength in integration testing, data integrity validation, and diagnosing calculation/automation defects using controlled test data and scripted date emulation; experienced with JIRA/TestRail and Selenium-based regression coverage.”

Carlotta Island

Screened ReferencesStrong rec.

Junior Frontend Engineer specializing in React and FinTech web applications

3y exp

“Developer who uses AI as a practical collaborator rather than a crutch, pairing tools like Claude with console logging, testing, and hands-on validation. They emphasize understanding code, data flow, and architecture while staying current by building projects and following AI and engineering communities.”

“Built a production AI-native internal onboarding feature that reduced manual product setup effort by combining barcode API data, product photos, structured LLM outputs, and a polished real-time camera UI. Demonstrates hands-on experience across the full stack of LLM systems: prompt/schema design, multimodal inputs, backend orchestration with SQS and vector retrieval, and production reliability through evals, telemetry, and drift monitoring.”

“Backend-leaning full-stack engineer who has built AI-powered analytics products from 0→1, including a predictive analytics dashboard and an AI orchestrator for natural-language-to-database querying. Particularly strong in making LLM systems production-safe through schema validation, self-healing retries, monitoring, and retrieval optimization, with quantified impact on cost, latency, and quality.”

“Built across cloud infrastructure, AI-powered product workflows, and backend data reliability in environments including Northeastern, Knead, and Grafx. Particularly compelling for roles needing someone who can both ship AWS-based systems end-to-end and debug messy production issues involving caching, APIs, and data pipelines.”

Sophie Adams

Screened ReferencesStrong rec.

Senior Full-Stack Engineer specializing in event technology and interactive systems

New York, NY20y exp

“Full-stack product engineer in event tech who has owned AI-powered web products from architecture to live production, including a no-code SaaS/marketplace for event activations and real-time AI kiosk experiences. Particularly strong in building for non-technical users in high-stakes live environments, with hands-on experience across Vue/Laravel, LLM workflows, image generation pipelines, and operational reliability.”

“ML/AI engineer with hands-on ownership of production recommendation and GenAI systems, spanning experimentation, deployment, monitoring, and iteration. Stands out for delivering measurable outcomes—22% CTR lift, 15% conversion lift, and a 30% reduction in support tickets—while demonstrating strong judgment on latency, cost, and safety tradeoffs in real-world systems.”

“Frontend engineer/lead who has owned end-to-end architecture for large customer-facing React/Next.js platforms, emphasizing strong API contracts (GraphQL + TypeScript codegen), automated quality guardrails, and performance as a feature. Built complex workflow UIs including a multi-step patient booking flow for New York’s largest healthcare provider and an admin dashboard handling 400,000+ USDA ingredient records, with disciplined state management, staged rollouts, and real-user monitoring.”

“Full-stack engineer with strong Next.js App Router/TypeScript experience who built production dataset search/filtering and data-heavy dashboards backed by Postgres. Demonstrates hands-on performance work across the stack (EXPLAIN ANALYZE, composite indexes, caching, React profiling/memoization) and has built durable, Temporal-like orchestrated data-processing workflows with idempotency and retry strategies in an early-stage startup environment (Gaia AI).”

“Software engineer with hands-on experience shipping production AI agents and end-to-end ecommerce workflows. They built a customer support automation agent with strong guardrails and evaluation practices, then improved it post-launch using real user data to cut latency ~30% and token cost ~25%. Also drove a zero-to-one self-serve order modification product across React UI, backend services, and cross-functional alignment.”

“Early-career engineer who built and launched a zero-to-one AI-driven approval workflow at SDSU that is used daily by roughly 2,000 university users. They owned the system end-to-end—from FastAPI/PostgreSQL backend to React UI—and showed strong judgment around LLM reliability, using a two-step pipeline, validation checks, and human-review fallbacks to cut manual processing time by about 80%.”

Laxminarayana Yaga

Screened

Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps

Missouri, USA4y exp

“Built and deployed a production RAG pipeline at PNC Financial Services to let risk/compliance analysts query millions of internal financial documents in natural language, reducing manual search and speeding regulatory validation. Demonstrates deep practical experience with large-scale document ingestion/OCR cleanup, retrieval performance tuning (hierarchical indexing, caching), and LLM reliability controls (grounding, citations, abstention), plus cloud orchestration on Azure and AWS.”

“Frontend engineer who led an enterprise customer-facing analytics dashboard end-to-end, handling complex visualizations, real-time updates, and high-performance requirements. Demonstrates strong architecture and scale practices across React/TypeScript, Redux Toolkit/RTK Query, GraphQL, and performance techniques like virtualization and code-splitting, plus disciplined QA/rollout with feature flags, Cypress, Sentry/New Relic, and Lighthouse.”

“Built a production AI-powered sales automation system that reads inbound product enquiry emails, extracts structured data, and routes decisions via a rules-based workflow integrated with a product database. Leverages Gemini structured outputs/schema plus option-based prompting and validation to keep responses reliable, and optimizes latency by breaking agent reasoning into smaller LLM calls; evaluates workflows with LangSmith and metrics like completion rate and accuracy.”