Vetted API Development Professionals

Sreedivya Nagalli

Screened

Junior AI/ML Engineer specializing in deep learning and full-stack ML applications

2y exp

“Built and operated a production-used RAG-based AI study planner (GPT-4 + FAISS) that handled 250+ concurrent users, with real-world reliability engineering (caching, fallbacks, schema validation, Redis state, monitoring). Also has healthcare data integration experience at Medinet Analytics, standardizing messy EHR/practice-management data with canonical schemas, idempotency hashing, and compliance-grade audit trails.”

Manali Patil

Screened

Senior Software Engineer & Engineering Manager specializing in cloud backend and manufacturing MES

Santa Clara, CA9y exp

“Customer-facing engineer who led recurring midnight ERP data-feed/B2B integrations from prototype to production, building reusable APIs and using Hangfire for job scheduling. Known for tight weekly customer iteration, strong documentation and test coverage (80%+), and cross-functional problem-solving with Operations/Quality/NPI to resolve data-collection and manufacturing-process constraints; has 2 customers live on the integration.”

Sravya Chunduri

Screened

Mid-level AI/ML Engineer specializing in LLM, NLP, and MLOps

Virginia, USA4y exp

“AI/ML Engineer with 3+ years of experience spanning RAG pipelines, MLOps, large-scale data workflow automation, and resilient Playwright-based UI automation. At Black Hawk Network and Wipro, they describe shipping production systems with strong observability and compliance controls, including reducing flaky automation failures from 30% to under 2% and automating 3+ TB/day reconciliation workflows.”

Henry Li

Screened

Junior Software Engineer specializing in mobile, full-stack, and game development

California, USA2y exp

“Engineer with hands-on experience shipping production-critical AI-assisted workflows, including an internationalization system for a React Native/Expo mobile game that expanded from English-only to 15 languages. Stands out for pragmatic, safety-first use of AI: they design deterministic validation layers, constrain model scope, and prioritize architecture, QA, and maintainability over hype-driven agent complexity.”

“Founding engineer who built a multi-tenant AWS backend from scratch focused on ultra-fast, configuration-driven client onboarding and low operational cost. Automated tenant provisioning/deployments with Terraform + GitHub Actions (new client infra in ~13 minutes) and scaled to 62 production clients handling ~75k requests/day without a major rewrite. Hands-on with migrations (DynamoDB->MongoDB), reliability/observability, and performance tuning (indexes, Redis, queueing, connection management).”

“Backend-leaning full-stack engineer who has built and operated event-driven microservices platforms (FastAPI/React/TypeScript, Kafka, Kubernetes) and internal DevOps tooling. Delivered measurable impact through user-feedback-driven iteration (WebSockets update mechanism cutting redundant API calls ~30%) and operational improvements (deployment monitoring dashboard reducing rollback time ~40%), with strong focus on reliability, observability, and data consistency at scale.”

“Built Hexagon’s production Text-to-CAD Copilot that converts text and rough sketches into editable CAD code, combining GraphRAG (Neo4j/LangChain) with a Gemini-powered vision module and multi-agent geometric validation—cutting manual modeling from a day to ~45 seconds and driving retrieval latency below 50ms. Also has large-scale GCP data/ML orchestration experience (Airflow/Cloud Composer, Dataflow, Pub/Sub, Snowflake) processing 50M+ daily records with drift monitoring and automated reliability controls.”

“Backend engineer with airline operations domain experience who modernized flight-ops systems from batch updates to real-time streaming on AWS (Kafka + Spring Boot microservices), improving latency and stability through metric-driven tuning and idempotency. Also shipped a production LLM decision-support component using RAG over operational logs and internal procedures, with strong guardrails and an evaluation/regression loop to reduce hallucinations and enforce grounding.”

“Backend/data engineer with hands-on experience building end-to-end data pipelines and Spring Boot services, including systems processing up to 1 million records per day. Demonstrates practical strength in reliability engineering, API versioning, external data ingestion, and early-stage delivery with CI/CD, observability, and pragmatic architecture choices.”

“Full-stack engineer who has owned an operations/reporting dashboard end-to-end, spanning React/TypeScript frontend architecture, ASP.NET Core APIs, and SQL data access. Stands out for combining strong UI performance optimization with pragmatic backend decisions, post-launch monitoring, and 0→1 startup platform building that improved API speed by 35% while supporting 2,000+ transactions per hour.”

“Built an AI-driven trading mentor/analytics system combining a React frontend, Spring Boot backend, and FastAPI ML service for stock risk and indicator analysis. Stands out for a pragmatic approach to AI-assisted development: uses AI for acceleration, but manually reviews code line by line, validates outputs against real market behavior, and adds safeguards for unreliable financial data.”

“Product-focused candidate with live B2C/product-led growth experience who translates free-to-play game mechanics (onboarding as a core loop, progressive unlocking, nudges, variable rewards) into real-world web/mobile products. Strong in live experimentation, KPI-driven iteration, and monetization/retention measurement (DAU/MAU, funnels, LTV-focused A/B testing).”

“Engineering leader for a facial-recognition edge platform who drove a full-stack modernization to scale from ~100 edge devices to thousands of locations. Led a major distributed-systems re-architecture (NATS streaming), database migration (MongoDB to PostgreSQL + TimeScaleDB), and UI/API modernization (React), while scaling the team 5→15 and implementing CI/CD + automated QA to improve release velocity and reliability.”

Saketh Kota

Screened

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

Irving, TX4y exp

“Built and productionized a RAG-based LLM research assistant for biomedical and regulatory document search using Mixtral 7B on SageMaker, LangChain, and Milvus, cutting research time by ~40%. Has hands-on multi-cloud MLOps experience across AWS/Azure/GCP with Kubeflow/Airflow/Composer plus Terraform + ArgoCD, and applies rigorous evaluation/monitoring (latency, accuracy, hallucinations). Also partnered with a non-technical PM to deliver an insurance policy Q&A chatbot that reduced customer response time by 30%+.”

“Built a production LLM-powered crypto trading platform in PHP/Laravel that combined RAG, market data, sentiment signals, and operator review workflows to generate buy/sell recommendations. Stands out for a disciplined approach to agentic systems: the application owns orchestration, risk controls, audit trails, and approvals, while the LLM is treated as a bounded advisory component rather than the control plane.”