Vetted API Design Professionals

“Backend engineer with healthcare-domain experience building a security-critical RBAC identity/authentication/authorization microservice suite used across hospital imaging platforms (X-Ray, Ultrasound, etc.). Demonstrates strong security mindset (mTLS, cert hygiene, JWT, pen-testing collaboration) and pragmatic scaling/reliability practices (Nginx load balancing, Redis caching, automated tests, canary rollouts).”

“Co-founded a Digital Loss Prevention (DLP) startup and raised $6M in seed funding by showcasing a controlled, laptop-based technology demo. Post-funding, drove MVP planning and execution by sequencing operations and assembling a team to build an appliance MVP, using an iterative build/evaluate/visualize approach.”

“Backend engineer with experience in both regulated healthcare and finance: built a multi-agent RAG system to generate FDA regulatory approval documents for biomedical devices, improving retrieval accuracy via hybrid search (semantic + BM25) and hierarchical chunking. Previously at JPMorgan Chase, led a Java microservice refactor and AWS migration using Elasticsearch-first patterns, caching, and safe rollout strategies (parallel runs, canary, blue-green) in asset/wealth management.”

“Engineering leader who drove TiVo IPTV’s client-facing API modernization from a monolith to AWS-based microservices (API Gateway, Lambda, EKS, Kafka, DynamoDB/RDS), including phased/blue-green production routing of millions of calls. Emphasizes org scaling through skill-based hiring, mentorship, and a you-build-you-run ownership culture, while balancing technical leadership with executive stakeholder communication and budgeting.”

“React-focused engineer who built a full-stack analytics/test-metrics dashboard (React frontend + Python backend) and turned common UI pieces (data tables, filter panels, chart wrappers) into a reusable internal component library with docs, examples, and basic tests. Strong on profiling-driven performance optimization (React Profiler, memoization) and on owning ambiguous internal-tool projects end-to-end; now planning to package internal patterns into public open-source components.”

“Owned and built Bloomberg’s end-to-end bitemporal dividend & dividend-forecast data platform powering BQL for 400k+ terminal users. Architected real-time Kafka ingestion (5k–10k msgs/sec) across 100k+ tickers with strong correctness guarantees (PIT/bitemporal time-travel, immutable history to avoid look-ahead bias) and achieved sub-100ms p95 query latency through indexing and caching, deployed with Kubernetes + DLQ and robust monitoring.”

Akanksha Agrawal

Screened

Mid-Level Full-Stack Software Engineer specializing in event-driven data platforms

Bangalore, India5y exp

“Backend engineer with SAP experience modernizing a legacy Flask/PostgreSQL product master data platform into a modular, stateless, containerized service with Kafka-based background processing and improved observability. Also has hands-on academic/side-project experience operationalizing ML (NLP retrieval with TF-IDF/BERT via FastAPI and CV lane-edge detection inference APIs using PyTorch).”

Anu Baluguri

Screened

Mid-Level Software Engineer specializing in cloud-native microservices and event-driven systems

San Francisco, CA4y exp

“Full-stack engineer with production experience at Atlassian and Zoho, spanning GraphQL federation, React/TypeScript frontends, and cloud-native AWS/Kubernetes operations. Built and operated a federated GraphQL gateway with Terraform + CI/CD + observability, delivering major latency and integration-time improvements, and also designed high-volume Kafka data pipelines (10M+ events/day) with strong reliability guarantees.”

Vismay Patel

Screened

Senior AI & Machine Learning Engineer specializing in NLP, GenAI, and MLOps

Berkeley, CA7y exp

“ML/GenAI practitioner with healthcare domain depth who built and deployed a production cervical-cancer EMR classification system using a hybrid rules + medical BERT approach, optimized for high recall under severe class imbalance and PHI constraints. Experienced running end-to-end production ML/LLM pipelines with Apache Airflow (validation, promotion/rollback, monitoring, retraining) and partnering closely with clinicians to calibrate thresholds and implement human-in-the-loop review.”

“Full-stack engineer with experience spanning Amazon IMDb and Northeastern’s NeuroJSON portal, combining consumer product work with complex scientific data applications. Built IMDb’s streaming providers feature—described as the company’s most impactful feature of 2023—and has hands-on experience with React/Angular, GraphQL, AWS, Python services, and production monitoring.”

“Built an AI-powered job outreach application for his own job search and took it from idea to production use, owning architecture, FastAPI backend, retrieval/generation pipeline, frontend workflow, deployment, and iteration. Especially compelling for teams needing a pragmatic full-stack engineer who can turn LLM-based product ideas into usable, maintainable tools with measurable workflow impact.”

“Built end-to-end LLM/RAG systems for biological data and scientific literature analysis in a drug discovery setting, helping researchers explore disease insights and treatment hypotheses faster. Combines applied GenAI product work with strong production engineering, including monitoring, retrieval optimization, reusable Python services, and scalable deployment on AWS/Kubeflow.”

Pavan Kishore Ramavath

Screened

Intern Software Engineer specializing in machine learning and backend systems

Leesburg, VA1y exp

“Built an AI-powered medical coding system at Clinpex that mapped 88,000+ clinical terms to standardized codes, achieving about 86% accuracy and cutting manual review time by over 80%. Brings hands-on backend ownership in a healthcare AI setting, with experience using semantic retrieval, LLM validation, and human review to handle ambiguity and reliability in a regulated domain.”

Arjun Kulkarni

Screened

Junior Software Engineer specializing in AI, LLM systems, and healthcare applications

San Francisco, CA3y exp

“Product-minded full-stack engineer with experience improving performance, UX, and platform architecture across startups including SideShift, Curator, Congruence, and work on an AI coding assistant called Exec. Stands out for cutting messaging-system Redis traffic by roughly 95%, redesigning user flows for faster adoption, and building reusable multi-tenant systems and cross-platform APIs without over-abstracting.”

“Backend-focused engineer with experience at AWS building a global alarm processing platform (Python, Lambda/SQS/DynamoDB) handling traffic spikes and reliability issues; resolved duplicate alerts and latency under load by fixing hot partitions and enforcing idempotency. Previously at Cognizant, built Java/PostgreSQL backend workflows for healthcare dashboards using pre-aggregated summary tables, strong SQL optimization, and state-driven job orchestration with ELK-based observability and production guardrails.”

“Player-coach engineering leader focused on consumer-grade video/multimodal products and high-reliability identity/auth experiences. Led design and implementation of multi-step mobile login/MFA flows with telemetry-driven funnel improvements, shipped Node services and security fixes, and owned auth incidents end-to-end using RUM and step-level instrumentation. Introduced feature-flagged delivery and targeted review/testing practices to speed iteration ~20–30% while keeping login stability high.”

Alex Vo

Screened

Staff Backend Software Engineer specializing in telemetry pipelines and observability

San Jose, CA3y exp

“Backend engineer from VMware focused on proprietary enterprise systems (monitoring tools, data pipelines, and APIs). Drove a ClickHouse migration POC (local to remote host) using a dual-write/cutover approach and source-level debugging across Node/driver differences during a Node 12→20 upgrade, and delivered measurable performance gains (~20% CPU/memory improvement) through batching and streaming ingestion.”

Harsh Sanas

Screened

Intern-level Software Engineer specializing in GenAI, RAG, and backend systems

San Francisco, CA2y exp

“AI/LLM engineer focused on shipping production-grade agents that automate support, sales intake, and ERP-connected workflows. Stands out for combining strong orchestration and guardrails with measurable business outcomes, including 45% faster support handling, ~$1.2M annual savings, 18% higher customer satisfaction, and 99.5%+ reliability in production.”

Akhil Kunala

Screened

Mid-level Software Engineer specializing in backend systems and cloud-native FinTech

Seattle, WA5y exp

“Amazon engineer with 5+ years of experience who built an AI-assisted log investigation and triage workflow that cut debugging time by about 30% during on-call incidents. Combines observability tooling like CloudWatch and Splunk with Python, prompt engineering, and RAG-based diagnostics, and has practical experience orchestrating agentic AI workflows with a strong human-in-the-loop reliability focus.”

“CMU candidate with hands-on experience taking LLM concepts from research prototypes toward production-ready designs (structured outputs, guardrails, failure-scenario evaluation). Also partnered with sales/customer teams at Mazecare to drive adoption with Dontia Alliance (largest dental clinic chain in Singapore) and engaged Singapore government stakeholders, bridging clinical workflow needs with IT security/integration concerns.”

“Cloud infrastructure/product engineer with end-to-end ownership of cloud-native storage/observability products, including taking an internal CMS to Google Cloud Marketplace and scaling to ~40,000 deployments. Strong in Kubernetes-based platforms (Operators, microservices, RabbitMQ) and performance/scalability work (e.g., 200% cluster capacity increase) plus internal tooling that materially improved SRE/QA debugging and release velocity.”

“Full-stack engineer building real-time internal banking operations dashboards (Java/Spring Boot microservices + React/TypeScript) with Kafka-based streaming and post-launch performance optimizations. Also shipped a production internal AI support assistant using RAG (Confluence/PDF/support docs ingestion, embeddings + vector DB retrieval) with guardrails, evaluation loops, and observability to reduce hallucinations and prevent regressions.”

Arun Gampala

Screened

Mid-level Full-Stack Developer specializing in MERN and AWS microservices

TX, USA4y exp

“Backend engineer with experience at MetLife and Amazon focused on security and control for internal and customer-facing services. Emphasizes contract-first Python/FastAPI APIs with strong auth (JWT + RBAC/claims), data-layer isolation (RLS/tenant scoping), and reliability practices like incremental refactors, rollback planning, and idempotency to handle retry-driven failure modes.”