Vetted Docker Professionals

“Built multiple AI-powered products from scratch, including ConnectAbility, an accessibility tool combining computer vision and LLMs to describe visual content for users with disabilities, and SpamBack!, a macOS app that detects scam texts and auto-generates responses. Stands out for full-stack/backend ownership of applied AI systems, especially around async workflows, inference performance, and reliability safeguards.”

Yashwanth J

Screened

Mid-level Software Engineer specializing in AI/ML and full-stack systems

Seattle, WA4y exp

“Engineer with Apple experience building LLM-powered internal workflow orchestration systems using Python, LangGraph, FastAPI, Redis, vector search, and Kubernetes. Stands out for a highly pragmatic, production-focused approach to agentic systems: deterministic state management, strong guardrails, observability, and human review for high-risk actions.”

“Senior frontend engineer focused on complex internal operations and payment products, with deep experience building React/TypeScript dashboards for payments, subscriptions, and observability workflows. Stands out for going beyond UI implementation to shape API contracts, real-time architectures, performance strategy, and product behavior across support, finance, and engineering use cases.”

“AI/LLM-focused engineer with hands-on experience building RAG pipelines, prompt engineering workflows, and multi-agent systems using tools like LangChain. Stands out for combining AI-assisted development with production-grade validation and for leading the architecture/orchestration of agent-based recommendation systems that improved response time, accuracy, and scalability.”

“Engineering leader with hands-on platform, cloud, security, and data infrastructure experience who managed a 9-person cross-functional team spanning data engineering, infrastructure, SRE/DevOps, and DevSecOps. Notable impact includes completing a Mesos-to-ECS migration, cutting AWS spend by $1.6M annually, standing up a security engineering team, and building a no-code data lake/warehouse platform in partnership with sales, support, data science, and BI.”

“Built and productionized a dual-agent LLM invoice-processing system for GFI Partners, adding guardrails and audit trails to earn stakeholder trust and drive adoption while cutting operational burden by 75%. Uses LangSmith observability to diagnose real-time workflow regressions and has experience teaching agentic AI concepts (e.g., at Carnegie Mellon) through hands-on, scaffolded demos.”

“Backend/data engineer with Optum healthcare claims domain experience building high-reliability Python microservices (FastAPI/Kafka/Postgres) and AWS data platforms (EKS, Glue, Redshift). Demonstrated strong production ownership: fixed duplicate Kafka processing via transactional outbox/idempotency, scaled to millions of daily events, and delivered major SQL performance gains (40+ min to <5 min, ~60% CPU reduction). Seeking remote-only work; targets $130k base.”

“Backend engineer who built gnocal, a ~150-line stateless Go service that turns on-chain event data into standards-compliant .ics calendar feeds consumable by Apple/Google Calendar, deployed on Fly.io. Also refactored MCTS into Monte Carlo Graph Search (Python-to-Rust) using deterministic tests and state canonicalization to handle transpositions, and implemented decentralized role-based ACLs in Gno for a smart-contract web hosting network (gno.land / All in Bits).”

“Backend/data engineer with production experience at Walmart and HealthSnap building Python services and data pipelines on AWS (EKS, Lambda, Glue, Airflow). Strong reliability and operations focus—implemented idempotency + circuit breakers for peak-traffic consistency issues, GitOps CI/CD, and observability. Demonstrated measurable performance wins (Postgres p95 45s to <5s, ~60% CPU reduction) and modernized SAS batch workflows to Python with parallel-run parity validation and feature-flagged rollout.”

“Built production LLM systems focused on reliability and safety, including a plain-English deployment tool that generates validated plans and provisions to Kubernetes while preventing unsafe actions via schema enforcement and plan/execute separation. Also created multi-LLM workflows (LangGraph) and stakeholder-friendly demos at Bosch, including a PyQt/FastAPI/CUDA app comparing SAM2 vs SAMWISE for on-device object detection with intuitive UX for business users.”

“Backend/data engineer who has owned production Python APIs and high-throughput async workflows on AWS (FastAPI, Docker, ECS/EKS/Lambda) with mature reliability practices like idempotency, bounded retries, circuit breakers, and strong observability. Also built AWS Glue ETL into an S3/Redshift lakehouse and modernized legacy batch systems via parallel-run parity testing and feature-flagged migrations, including a SQL tuning win cutting a multi-minute query to under 10 seconds.”

“Machine learning software engineer intern experience at Amazon, where they built a production testing framework to inject frames/videos onto devices to measure embedded CV model inference and ensure broad model compatibility via automatic NNA metadata handling. Also built side projects spanning LLM/RAG orchestration (LangChain/LangGraph with reranking and citations) and applied CV/healthcare work (nail disease detection, medical retrieval chatbot).”

“Full-stack engineer (Snowflake) who shipped an AI/LLM-powered data exploration product end-to-end, spanning Spring Boot/Python services and a polished React UI with streaming responses and robust fallbacks. Experienced operating high-scale AWS deployments (Docker/Kubernetes, SNS/SQS, RDS Postgres, CloudWatch, Jenkins CI/CD) supporting thousands to tens of thousands of concurrent users, including handling real traffic-spike scaling incidents.”

“ML/LLM engineer with production experience building a multi-GPU LLM inference platform using TensorRT and vLLM, achieving ~40% p95 latency reduction through batching/KV caching, quantization, and CUDA/runtime tuning. Also has end-to-end orchestration experience (Kubernetes, Airflow) and has delivered real-time fraud detection systems at Accenture in close collaboration with non-technical risk and product stakeholders.”

Swarnabha Roy

Screened

Mid-level Robotics & Computer Vision Engineer specializing in autonomous systems and edge AI

College Station, TX6y exp

“Robotics/perception researcher (MVOS Lab, South Dakota State University) who built an end-to-end multimodal RGB-D + LiDAR pipeline for autonomous greenhouse harvesting and 3D plant phenotyping. Demonstrated strong production ownership by diagnosing motion blur with ROS-bag + OpenCV metrics and shipping an edge-deployed, scan-quality-aware workflow that boosted barcode read rate to 98% and supported ~70% autonomous pepper detection/harvesting accuracy.”

Raghu Prabhudeva

Screened

Staff Embedded/Automotive Systems Architect specializing in IVI, ADAS, and digital cockpit platforms

USA

“Robotics-adjacent software engineer with hands-on ROS 2 experience building and integrating sensor nodes (IMU, GNSS, wheel encoder) and working with distributed pub/sub concepts via ROS IDL and DDS. Also has Gazebo exposure through Udacity coursework and uses Docker as a core development/deployment tool, with related experience in automotive camera-based solutions.”

Daniel Luzzatto

Screened

Junior Machine Learning Engineer specializing in LLMs, computer vision, and robotics

Tirat Carmel, Israel1y exp

“Built and deployed an agentic, multimodal LLM system that automates privacy redaction pipelines (audio/video/tabular) using LangChain orchestration and a closed-loop self-correction design. Personally implemented and performance-optimized core CV tooling (face blurring with tracking/Kalman filter) achieving >100 FPS on CPU, and validated reliability with golden-dataset benchmarking across 100+ privacy intents and measurable redaction metrics.”

greg farhadian

Screened

Senior Software Engineer specializing in cloud data platforms and Java microservices

Remote4y exp

“Backend/data engineer with experience building Kafka-driven real-time pipelines that support ML code deployment and downstream integrations. Currently migrating high-throughput mainframe (COBOL/assembly) processing to Java, using Spark/Databricks to preserve performance and employing rigorous A/B testing across dev/pre-prod/prod with years of historical data.”

Sandeep Rohilla

Screened

Principal Platform Engineer specializing in AI-driven document automation

San Francisco, CA20y exp

“Backend engineer who built an event-driven, multi-service resume review system integrating AI/ML workflows. Demonstrated strong performance engineering (e.g., composite indexing dropping latency from ~600ms to ~35ms and major P95 gains) and high-throughput pipeline optimization via caching, batching, and worker concurrency tuning, with multi-tenant isolation implemented across DB and Redis.”

Daksh Jain

Screened

Mid-Level Software Development Engineer specializing in AWS streaming media platforms

Portland, OR3y exp

“Full-stack engineer with hands-on Next.js App Router + TypeScript experience (built a RateMyProfessor-style platform end-to-end using RSC, dynamic routing, debounced search, and cache invalidation). Also has AWS backend depth—built a Step Functions-based wave rollout/feature access control framework for MediaPackage V2 with idempotency, retries, rollback, and ongoing correctness reconciliation.”

“Engineering leader with experience at CBS Interactive and Paramount who scaled a web engineering team from 4 to 30+ (adding QA/PMO/mobile) while instituting strong agile and code-quality processes. Led a major architectural shift to a generic content API that decoupled web/mobile experiences from niche publishing systems, reportedly cutting developer onboarding time by ~50% and enabling some day-one production commits.”

Yash Jajoo

Screened

Senior Software Engineer specializing in AI and FinTech platforms

New York City, NY8y exp

“Built a production LLM pipeline at Walter AI that scans massive user inboxes, identifies financial newsletters, and extracts trading strategies into structured JSON for downstream paper-trading workflows. Stands out for combining agent architecture with strong production discipline—cutting scan time from 20 to 5 minutes, reducing LLM costs by 90%, and achieving 3-second P99 latency while handling messy, inconsistent email data at scale.”

Richard Ferguson

Screened

Executive technology leader specializing in government, cloud, and cybersecurity

Reno, NV31y exp

“Founder with a bootstrapped startup who navigated early hiring and scaling by leveraging contractor talent before converting key contributors to full-time employees. Active in Northern Nevada's startup ecosystem through Generator and StartupNV, and notably declined angel/VC offers to preserve equity because of strong conviction in a differentiated product and its market fit.”