Vetted Distributed Systems Professionals

“Backend engineer with hands-on ownership of Python/Flask microservices and recommendation systems across edtech and telecom. Deployed and operated real-time personalization/recommendation platforms on AWS EKS with Jenkins-based CI/CD, GitOps-style declarative configs, and strong observability practices. Has migration experience moving legacy mixed environments to modern containerized Kubernetes and built Kafka pipelines feeding ML services while managing schema evolution.”

“Full-stack engineer with hands-on experience spanning real-time AI products, large-scale payments migration, internal research infrastructure, and open-source ML tooling. Particularly compelling is the mix of low-latency React/Node/TypeScript systems work, zero-downtime migration of 50,000 accounts across 12 regions, and proactive contributions to Kubeflow build and security reliability.”

Ajith P

Screened

Mid-level Backend Software Engineer specializing in AI workflow automation for finance and healthcare

4y exp

“Backend/AI engineer with healthcare domain experience who built a patient journey analytics API (FastAPI/PostgreSQL/Snowflake/Redis) and debugged peak-hour latency down from ~900ms to ~50ms via indexing and query optimization. Shipped an LLM-powered clinical summary/recommendation assistant end-to-end and designed a multi-step risk evaluation agent workflow with safety guardrails against hallucinations and unsafe outputs.”

“Recent Cornell graduate with roughly 18 months of unusually high-impact Solutions Engineering experience, already owning enterprise AI engagements for Citi and Samsung at WorldLink US. Stands out for combining technical pre-sales with hands-on prototyping and delivery, including local on-device AI for Samsung QA and helping take an AI security product from architecture to first $100K close.”

“Software engineer with recent hands-on production work across Go, Python, and React/TypeScript, spanning healthcare APIs, compliance systems, and data engineering. Stands out for delivering under ambiguity: replaced a legacy SOAP eligibility service, built a Kafka-based compliance pipeline handling 14,000 events per second, and created a modular banking data pipeline that cut month-end work by 30%.”

“Built and productionized an LLM-enabled system visualization web app at Longshot, designed modularly to pivot quickly to a mobile-friendly interface as customer needs changed. Experienced in diagnosing LLM/agentic workflow failures using observability, deterministic replay, and fault-tree root cause analysis. Also delivers developer-focused demos and trainings (including robotics deployment/mapping at Meta) and partners with sales as a technical closer, including for government clients by demonstrating failure modes and system modularity.”

“At Esri, the candidate is building a production LLM-powered WebGIS AI framework that embeds an AI assistant into web maps and routes natural-language requests into ArcGIS JavaScript SDK functions via a LangGraph-orchestrated, multi-agent system. They emphasize production reliability and scale (strict tool calling/JSON, live schema validation, query guardrails) and rigorous evaluation/observability using LangSmith, offline prompt datasets, and latency/tool-call accuracy tracking.”

Akashleena Sarkar

Screened

Junior Software Engineer specializing in robotics and real-time distributed systems

Mountain View, CA2y exp

“Robotics software engineer focused on low-compute navigation/SLAM: built a 6-DOF SLAM validation pipeline (IMU + 2D LiDAR + ultrasonic) producing ~1cm OctoMap accuracy and deployed it on an Intel Atom by optimizing particle-filter SLAM with a greedy max-likelihood update. Deep ROS 2 experience (executors, composable/lifecycle nodes, QoS, timestamping) plus simulation and deployment tooling (Gazebo C++ plugins, Docker, CI/CD, ROS 2 build farm) and drone navigation work with MAVROS/PX4.”

“Backend/AI engineer at Intuit who built a production AI-powered case assistant for support agents (FastAPI on AWS EKS) combining Postgres case data, OpenSearch retrieval with embedding reranking, and internal LLMs. Improved peak-season reliability by diagnosing P95/P99 timeout spikes and cutting P95 latency from ~800ms to <400ms via composite indexing, keyset pagination, connection pool tuning, and caching, while adding grounded-generation guardrails (evidence packs, confidence thresholds, fallbacks, human-in-the-loop).”

Hongxi Chen

Screened

Intern Software Engineer specializing in distributed systems and backend infrastructure

Beijing, China0y exp

“Backend engineer with deep experience building event-driven logistics systems (orders, warehouse execution, real-time delivery tracking) using Spring Boot/PostgreSQL/Redis and strong observability (Prometheus/Grafana). Led a zero-downtime migration from monolithic MySQL to a sharded architecture for ~2M users with dual-write, checksum validation, and fast auto-rollback, and has strong security expertise including PostgreSQL RLS for multi-tenant SaaS and robust OAuth/JWT handling.”

Luis Mirantes

Screened

Executive engineering leader specializing in scaling ML platforms and FinTech systems

Chevy Chase, MD30y exp

“Founder of an AI-agent startup focused on high-speed train braking efficiency, with a subsystem already demoed to RENFE Spain and positive initial feedback. Has hands-on fundraising and investor presentation experience, and stands out for a highly analytical approach to venture building centered on ROI, competitive barriers, IP protection, and evidence-based decision making.”

“ML engineer with production experience across healthcare and fraud domains, including end-to-end ownership of a telecare patient deterioration system at Oracle Health and a GPT-4/RAG fraud reporting solution at Cognizant. Stands out for combining scalable data/ML infrastructure, clinical NLP, and GenAI delivery with measurable gains in model quality and workflow efficiency.”

“Built and shipped a production LLM-powered internal developer tool that accelerated code reviews by about 30% while maintaining reliability through modular orchestration, validation, and monitoring. Demonstrates strong practical depth in agent architecture, backend workflow orchestration, and observability for non-deterministic AI systems, with concrete examples of reducing agent errors by 60%.”

CHIRAG ARORA

Screened

Senior Backend Software Engineer specializing in microservices and cloud platforms

San Jose, CA9y exp

“Backend engineer with PayPal experience building a high-reliability onboarding API platform (Java/Spring Boot) integrating KYC/compliance and serving 1M+ users annually. Also shipped an internal LLM-driven developer tool that automates PR review insights and OpenAPI documentation with rigorous evaluation, schema-bound guardrails, and production observability.”

“Backend/data engineer with hands-on production experience across FastAPI/PostgreSQL APIs and AWS (Lambda, ECS) delivered via Terraform + GitHub Actions. Built Glue-based ETL pipelines into Redshift with schema evolution and data quality checks, modernized legacy reporting into Python microservices, and has demonstrated measurable SQL performance wins (multi-second query reduced to sub-300ms).”

“Data engineer who has owned end-to-end production pipelines ingesting ~500GB/day from APIs/databases/Kafka into an S3 data lake (Glue/Spark) with Airflow-orchestrated Great Expectations quality gates. Built resilient external data collection systems with idempotent jobs, exponential-backoff retries, raw data capture, and backfills; also shipped Snowflake-backed APIs with caching, versioned endpoints, and backward-compatible data contracts. Led an early-stage Azure data platform build with phased delivery and GitHub Actions CI/CD, resolving schema-mismatch incidents quickly without downstream corruption.”