Vetted Kubernetes Professionals

“Full-stack TypeScript engineer who has owned a real-time workflow/communication platform end-to-end in production (Node/TS + React, Postgres/Redis, Kafka, Docker/CI/CD). Demonstrates strong distributed-systems pragmatism—designing for failure with retries, DLQs, idempotency keys, and atomic writes—plus operational practices like structured logging, monitoring, and zero-downtime deployments.”

“Product/UX designer who built a cloud-based data management and visualization system for healthcare and manufacturing, translating script-driven and highly technical workflows into guided, step-based experiences. Strong in progressive disclosure, role-based defaults, and trust-building UI patterns, with hands-on prototyping in Figma and close design-engineering collaboration (HTML/CSS, component systems, working TypeScript familiarity) to ship scalable, accessible designs.”

“Backend/data engineer with strong AWS production experience spanning serverless APIs and containerized workers (Lambda, API Gateway, ECS) plus data pipelines (Glue, S3, Athena/Redshift). Has modernized legacy SAS/cron batch systems into Python/AWS with parallel-run parity validation and low-risk cutovers, and has owned ETL incidents end-to-end (CloudWatch detection, backfills, and preventative controls). Targeting $130k–$150k base and strongly prefers remote, with occasional Bethesda onsite acceptable.”

“JavaScript engineer with open-source library contribution experience, including diagnosing a validation-related bug, shipping a tested fix, and improving documentation with practical examples and edge-case guidance to reduce repeated community questions. Emphasizes profiling-driven performance work, small safe refactors, and proactive ownership in fast-moving, unstructured teams.”

SAI PRASHANTH

Screened

Mid-level Full-Stack Software Engineer specializing in cloud-native microservices and FinTech

USA5y exp

“Software engineer/product-minded builder who owns customer-facing products end-to-end and ships in 1–2 week increments using CI/CD, automated testing, and feature flags. Built a TypeScript/React/Node platform that cut page load times by 40% and scaled to 3x concurrent users, and designed RabbitMQ-based microservices with Prometheus/Grafana monitoring. Also delivered an internal real-time support analytics dashboard that reduced response times by 30%.”

Omar Rahmoun

Screened

Senior Software Engineer specializing in distributed systems and AI platforms

Chicago, IL5y exp

“Senior engineer transitioning into a lead engineer role who is actively overseeing 5 developers and championing an AI-first development culture. Stands out for a highly structured approach to AI-assisted software delivery, including context engineering, phased planning, multi-agent orchestration, and deliberate hallucination mitigation rather than 'vibe coding.'”

“Backend/data platform engineer who owned an enterprise Django REST + PostgreSQL reporting backend and built Python ETL pipelines to normalize 3M+ legacy customer records, improving data reliability by 85%. Strong Kubernetes/GitOps practitioner (Helm, ArgoCD, Jenkins/GitHub Actions) with real-world production debugging experience, plus Kafka streaming at 5M events/day and a zero-downtime monolith-to-event-driven microservices migration on AWS that cut infra costs by 42%.”

“Full-stack product engineer who has shipped customer-facing features end-to-end, including a product detail page backed by Java/Spring Boot microservices and a React/TypeScript UI. Demonstrated measurable impact through performance and maintainability improvements (30% faster APIs, 25% less duplicated UI code, 40% reduced API complexity via GraphQL) and has operated/scaled apps on AWS with CI/CD, monitoring, and incident-driven scaling fixes.”

“Full-stack engineer who built a live-streaming edtech platform at KratosIQ, owning the entire frontend and the backend streaming layer. Notably migrated the system from a P2P mesh to an SFU architecture to handle scaling under heavy load, and delivered measurable React performance gains (450ms to 40ms render time) validated via Lighthouse and web vitals.”

Sachin Kulkarni

Screened

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

New York, US3y exp

“Recent master’s graduate in robotics with applied experience across reinforcement learning and ROS 2 autonomy stacks. Built an RL-based drone vertiport traffic controller (PPO) focused on reward design and simulation integration, and has hands-on navigation work in ROS 2 including LiDAR preprocessing, SLAM/path planning, and stabilizing TurtleBot3 wall-following. Also brings deployment experience containerizing robotics nodes and scaling them with Kubernetes on AWS.”

“Robotics software engineer focused on backend/integration for indoor autonomous mobile robots, with hands-on ROS 2 experience integrating Nav2/AMCL/TF2 and LiDAR/camera pipelines. Emphasizes production readiness—robust failure recovery, QoS-tuned distributed communication, and strong observability (logging/health checks)—validated through Gazebo simulation, sensor-data replay debugging, and Docker-based CI/CD deployment.”

“Backend/data platform engineer who owned a near-real-time patient feedback ingestion system, building a FastAPI + Kafka service with Snowflake/Airflow orchestration. Demonstrates strong production Kubernetes/GitOps practices on AWS EKS (Helm, Argo CD, Sealed Secrets) and solved real-time data integrity issues via idempotent processing with Redis.”

“Data engineer with ~4 years of experience building Python-based data ingestion/processing services and real-time streaming pipelines (Kafka/PubSub + Spark Structured Streaming). Has deployed containerized data applications on Kubernetes with GitLab CI/Jenkins pipelines and applied GitOps to cut deployment time ~40% while reducing config drift. Also supported a legacy on-prem data warehouse/backend migration to GCP using phased migration and parallel validation to meet strict reliability/SLA needs.”

Thanuja Mudududla

Screened

Mid-level Software Engineer specializing in full-stack cloud and agentic AI systems

San Jose, CA3y exp

“Backend engineer with hands-on ownership of production systems across maritime tracking, HR tech, and AI-powered document workflows. They combine strong operational instincts with measurable impact—cutting API latency from 10s to 3s, improving query performance by 60%, reducing deployment time by 50%, and driving 70% infrastructure cost savings with serverless design.”

“Built and deployed a production, multi-tenant modular agentic AI platform at Easybee AI, using LangChain/LangGraph with Redis-backed durable state to make agents reusable, traceable, and auditable. Emphasizes reliability via strict tool schemas, deterministic controllers, tenant-level policy enforcement, and regression testing derived from real production failures; also delivered AI automation for legal/finance workflows (attorney draw and expense automation) with explainable, deterministic payouts.”

“Backend engineer who owned and shipped a campaign analytics API (FastAPI/Postgres/Redis/Celery) with ingestion from Instagram/YouTube, JWT auth, tests, and Docker deployment; improved performance from >1s to <150ms using precomputed aggregates and composite indexes. Experienced with Kubernetes GitOps using GitHub Actions + ArgoCD (zero-downtime rollouts, one-click rollbacks), Prometheus/Grafana observability, hybrid cloud-to-on-prem migrations, and real-time notification streaming via Redis Pub/Sub + WebSockets.”

“Backend/AI engineer who has built and operated production Node.js/Express services on AWS (Postgres/Redis) and has hands-on experience shipping an AI-powered support agent using RAG (Pinecone + LLM) with grounding checks and evaluation for hallucination rate. Demonstrates strong production reliability/performance debugging, including reducing peak latency from ~2s back to sub-300ms through query and caching optimizations, plus designing agent workflows with retries and human-in-the-loop escalation.”

“Candidate is actively exploring AI-driven development and modern coding assistants like Claude AI, Cursor, Gemini, and other LLM-based tools. While they have not yet led AI-agent projects or used multi-agent systems in production, they are proactively building knowledge through AI coursework and the Microsoft AI Engineering specialization.”

Ashutosh Srivastava

Screened

Senior Engineering Manager specializing in AI platforms and cloud-native backend systems

Sunnyvale, CA11y exp

“Player-coach engineering leader who stayed hands-on (coding/reviews) while leading delivery, including designing an event-driven AI workflow engine with explicit state modeling and robust retries. Built near real-time enterprise analytics for campaign measurement and drove reliability/process improvements (observability, incident runbooks, release management). Introduced lightweight CI/CD and automated testing to cut release time by ~40% while maintaining quality.”

“Infrastructure/platform engineer with hands-on experience building and operating AWS Kubernetes environments with Terraform, including blue/green upgrade strategies and observability (Grafana/Prometheus). Modernized a mission-critical PostgreSQL system from legacy Sun SPARC hardware to x86/KVM with SAN and then redesigned it for load-balanced HA failover, and has operated hybrid on-prem (vSphere) to AWS connectivity using BGP/VPN and later Direct Connect, including post-incident improvements to centralized network config management.”