Vetted Kubernetes Professionals

“Software engineer with hands-on production experience deploying Spring Boot services to AWS using Docker and Jenkins CI/CD, focused on stable releases, easy rollback, and performance improvements through monitoring/logging and query optimization. Has proven cross-layer troubleshooting skills (identified packet loss causing intermittent timeouts via network traces) and experience collaborating on-site with operators in industrial/IoT-style environments, including working alongside robotics/PLC teams.”

“Built and deployed a production AI accessibility platform that turns chart and image-based graphs into real-time audio narratives for visually impaired users. Implemented a ResNet-based CV + OCR + NLP + TTS pipeline and improved performance through preprocessing, Redis caching, and Kubernetes autoscaling/rolling updates on AWS to handle traffic spikes with no downtime.”

“Graduate-school lab engineer who built and owned the final architecture of a Microservices Hub that integrates REST APIs, issues API keys, monitors 10+ Linux servers, and visualizes service dependencies via a topology graph. Strong in bridging legacy and modern stacks (Dockerized and non-Dockerized services like Apache/screen) using deep Linux/networking knowledge, plus practical real-time audio streaming for STT/TTS and experience mentoring others.”

“Backend Python engineer who owned the full backend build of an AI-driven platform for UK golf clubs, including FastAPI microservices, vector search, and a tuned LangChain+Pinecone RAG pipeline focused on cost and hallucination reduction. Experienced deploying Django/FastAPI/Flask stacks on AWS-backed Kubernetes with GitOps/ArgoCD-style delivery, plus executing legacy-to-AWS migrations and building Kafka-based real-time analytics pipelines.”

“Full-stack engineer with strong production experience improving performance and reliability of data-heavy analytics products. Has shipped end-to-end features spanning Node/Express + PostgreSQL + Redis and React/TypeScript, deployed via Docker/GitHub Actions to AWS EKS with Helm, and monitored with Datadog/CloudWatch; also built a Python compliance automation backend for AWS security monitoring with RBAC, versioned REST APIs, and resilient throttling-aware processing.”

“Built and shipped a production LLM-powered incident response agent for a microservices platform, automating alert triage and safe remediation recommendations with strong guardrails (RAG grounding, structured JSON outputs, rule-based validation, and human-in-the-loop). Implemented state-machine orchestration (Redis/Kafka), comprehensive eval/monitoring, and an error categorization pipeline that cut hallucination errors ~40% and reduced MTTR ~30%.”

Shreya Sivakumar

Screened

Mid-level Machine Learning & Generative AI Engineer specializing in AI agents and LLM workflows

4y exp

“Customer-facing AppSec/solutions engineer with experience securing cloud-native AI/LLM deployments on Azure and Kubernetes. Led threat modeling and production hardening (Key Vault secrets migration, least-privilege IAM, rate limiting, structured logging/monitoring, LLM guardrails) and has supported retail search/catalog platforms using Elasticsearch, including performance triage and rollout playbooks that improved customer trust and enabled engagement expansion.”

Carlos Cortés

Screened

Junior Robotics Engineer specializing in ROS 2, SLAM, and simulation

Bogota, Colombia3y exp

“Robotics software engineer with ~3.5 years in ROS/ROS2 mobile robotics, SLAM, and control who owned end-to-end integration for a sim-to-real mobile platform (Zephyr), including ros2_control, EKF sensor fusion (IMU + Vicon), and Gazebo validation with quantified accuracy. Also built a multi-drone CSLAM stack integrating ORB-SLAM3 and PX4 offboard control, scaling via namespaces, synchronization/QoS discipline, and performance debugging with ros2_tracing.”

Gly Vu

Screened

Junior Software Engineer specializing in full-stack tools and LLM inference infrastructure

3y exp

“Full-stack/edge-focused engineer who took a manual, terminal-based AI calibration workflow and turned it into a web-enabled remote calibration system designed for low-bandwidth 5G field deployments, now used across 85+ field sites. Experienced operating edge fleets with versioned rollouts, Kubernetes-based cloud monitoring, and Prometheus/Grafana observability, plus refactoring fast-moving AI codebases for modularity and strong typing.”

Nischit Patel

Screened

Mid-level Software Development Engineer specializing in Python, APIs, and AWS

NC, US5y exp

“Backend engineer with experience modernizing legacy systems and building modular Python/Flask services, including a REST-to-GraphQL migration for an e-commerce platform that improved API response time by 45%. Strong in performance and scalability work across PostgreSQL/SQLAlchemy (indexing, JSONB, N+1 fixes, connection pooling) and high-throughput systems (Celery + Redis), plus integrating ML microservices with TorchServe, Kafka streaming, feature stores, and Prometheus/Grafana monitoring.”

Pallavi Deshmukh

Screened

Mid-level Full-Stack Software Engineer specializing in React, Node.js, and Android media SDKs

USA5y exp

“Backend/data engineer who built an end-to-end real-time stock analytics platform: ingesting multi-source market data via Kafka/APIs, transforming it into dashboard metrics (e.g., Bollinger Bands), and storing in BigQuery/MySQL. Strong DevOps/GitOps experience deploying Python/Node microservices on Kubernetes with Docker/Helm, CI/CD (GitHub Actions/Jenkins), and ArgoCD, plus hands-on troubleshooting and migration work.”

Akram Ali

Screened

Senior Full-Stack Developer specializing in Node.js/TypeScript, cloud, and data engineering

Chicago, United States10y exp

“Frontend/fullstack lead who inherited a messy psychological app with production issues, drove a rapid stabilization (2–3 weeks) and major performance/architecture overhaul (Redux Toolkit, memoization, caching, lazy loading, CDN offload to S3/CloudFront). Also owns delivery and infrastructure practices (multi-env, Docker, GitHub Actions CI/CD, AWS ECS + load balancing) and led a 1-week POC for an AI-powered trucking management system (app.neblo.ai).”

Tanvi Kulkarni

Screened

Intern Full-Stack Engineer specializing in Java, React, and cloud-native backend systems

Santa Clara, CA1y exp

“Frontend-focused engineer with startup experience (SmartPath, OPC AI) who owned and evolved an internal React/TypeScript component library treated like OSS—refactoring core form and API wrapper modules for stability, type safety, and smaller bundles. Comfortable diagnosing production issues via logs/API traces and shipping end-to-end fixes with tests and documentation, including internal workshops to drive adoption.”

Revanth P

Screened

Mid-level DevOps Engineer specializing in AWS, Azure, Kubernetes, and GenAI infrastructure

Walnut Creek, CA4y exp

“Database/platform engineer with stronger hands-on experience in AWS and Azure than GCP, but able to speak credibly about cloud database architecture, automation, and reliability engineering. They led an on-prem MySQL to RDS/DynamoDB migration, built Terraform/Python-based zero-touch database operations, and described a performance incident where latency dropped from 2s to under 300ms while supporting 2x traffic.”

“Built and shipped Site Audit AI, a production multi-turn Claude-based agent that autonomously crawls websites, calls tools, and generates scored audit reports—reducing a manual 2-3 hour developer workflow to under 60 seconds. Also brings practical experience integrating inconsistent payroll/HR data across platforms like QuickBooks and Keka, with a strong focus on validation, fault tolerance, and resumable workflows.”

“Backend/AI engineer with deep healthcare experience building production Python microservices that turn raw clinical audio into structured notes and insights. They owned systems end-to-end across architecture, launch, monitoring, and incident response, with measurable impact including 40% lower operating costs, 22% better latency, and 99.9% uptime in a regulated environment.”

“Built an end-to-end agentic population health strategy copilot for healthcare leadership, turning broad chronic disease questions into structured, evidence-backed strategy briefs. Stands out for combining healthcare domain knowledge with production-grade GenAI implementation, including LangGraph orchestration, Databricks/MLflow deployment, human review, and quality gates focused on citations, metrics, risks, and safety.”

“Backend/AI engineer who built an enterprise RAG chatbot over 40,000+ technical documents, owning the system from ingestion and retrieval design through launch, optimization, and incident prevention. Stands out for treating LLM reliability as a data, retrieval, and observability problem—delivering 90%+ benchmark accuracy, ~50% fewer hallucinations, and major gains in lookup speed and latency.”

Nikita Sipeikin

Screened

Mid Backend Java Developer specializing in high-load FinTech systems

Bali, Indonesia5y exp

“Java developer with hands-on experience using AI coding tools heavily in day-to-day development, estimating roughly 60% AI-generated output in a recent shipped feature. Brings practical debugging and testing experience, including performance testing and resolving Spring Boot circular dependency issues through architectural changes.”

“Robotics software engineer who built a ROS 2 Humble autonomous hospital-equipment detection/localization robot end-to-end in Gazebo (custom worlds/models, Nav2 waypoint navigation, YOLOv8n perception, TF2-based depth fusion) and solved real-time integration issues via multithreading and QoS tuning. Also implemented and tuned an MPPI controller to enable smooth reverse parking on an OpenPodCarV2 platform, including real-world reverse engineering and hardware/software debugging.”

“Built production LLM/agent systems for procurement and contract spend controls, including a proactive contract value leakage detection platform that moved an organization from reactive audits to pre-payment rejection. Combines multi-agent orchestration (Semantic Kernel/LangChain/AutoGen), document AI benchmarking (Textract vs Azure DI), and MLOps/testing (MLflow, QTest/Pytest) with strong security practices (RAG-grounded responses to prevent prompt injection). Integrated anomaly alerts directly into SAP SES workflows and Power BI dashboards, citing ~$38M leakage addressed across large spend environments.”

“Product-minded full-stack engineer with hospitality tech experience who owned and scaled a multi-region guest verification/check-in workflow (ID/passport scanning, OCR, and government submissions) and built internal tools that cut manual entry up to 80%. Also built a React/TypeScript + FastAPI RAG “second brain” with async ingestion workers and an event-driven e-folio email microservice hardened with idempotency and retries.”