Vetted Docker Professionals

Bala Venkateswarlu K

Screened

Mid-level Data Scientist specializing in Generative AI, NLP, and MLOps

USA5y exp

“Built and deployed an LLM-powered claims-document summarization system (insurance domain) that cut agent review time from 4–5 minutes to under 2 minutes and saved 1,200+ hours per quarter. Hands-on across orchestration and production infrastructure (Airflow retraining DAGs, Kubernetes, SageMaker endpoints, FastAPI) and recent RAG workflows using n8n + Pinecone, with a strong focus on reliability, cost, and explainability for non-technical stakeholders.”

Vamshi Korra

Screened

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

Remote, USA5y exp

“Open-source React dashboard/visualization library maintainer focused on runtime performance and API clarity. Led a significant effort to eliminate severe render lag on large live-updating datasets through profiling-driven refactors (normalized state, memoized selectors) and locked improvements in with CI, linting, and documentation that reduced regressions and improved external contributor onboarding.”

Brian Cho

Screened

Mid-level Robotics Researcher specializing in robot learning and surgical robotics

Salt Lake City, UT7y exp

“Robotics software/ML engineer who led an end-to-end transformer-based real-time 3D shape prediction system for a tendon-driven continuum robot on a KUKA arm, including ROS2 multi-camera RGB-D data collection, multi-view calibration, and optimized ICP point-cloud registration. Also optimized an online sensing + motion planning loop for robot-assisted surgery using Bayesian Hilbert maps and A* search, and has Gazebo + RL experience for a robotic salamander.”

“Internship experience owning a customer configuration-data migration from legacy to a new system, building a Python pipeline with strong validation, logging, and quarantine patterns to handle messy real-world API data. Also leveraged an offline/bounded LLM-assisted workflow to generate candidate parsing/validation logic and edge cases, then hardened it into deterministic production code to speed development and reduce migration errors.”

“Backend engineer with experience at KPMG evolving an audit/reporting platform from monolithic components to microservices (Spring Boot/Node.js), improving API performance and enabling independent deployments. Demonstrates strong production focus across secure API design (FastAPI, JWT/OAuth2, RBAC/RLS), incremental migrations with feature flags, and robustness improvements like optimistic locking to prevent race conditions.”

“Backend engineer who built the core backend for Air Kitchens’ discovery/booking platform on AWS (Node + Python, DynamoDB, SQS/Lambda), optimizing for fast user-facing APIs and scalable async workflows. Introduced an AI matching service with a deterministic pre-filter + LLM ranking approach to balance latency vs quality, and has hands-on experience with production security (JWT/RBAC/RLS), CI/CD, and blue-green, staged migrations from Django to modular services.”

“Robotics software engineer who owned the navigation/control stack for a commercial autonomous lawnmower at Wavemaker Labs, with hands-on sim-to-real tuning and real-world debugging. Experienced in ROS 1/ROS 2 path tracking (Pure Pursuit), including adapting ROS 2 planner code into a ROS 1 system, and building telemetry/logging to quantify tracking errors. Currently in an ADAS simulation team enabling algorithm teams with SIL/HIL and feature validation.”

“Robotics software engineer focused on system-level C++/Linux stacks for multi-robot platforms, owning the communication layer and validation/testing infrastructure. Built Python simulation/replay and fault-injection tooling integrated with Docker + GitLab CI/CD, and debugged real-time localization issues by instrumenting IPC timing and refactoring multi-threaded pipelines for deterministic performance.”

“LLM/agentic systems practitioner who repeatedly takes customer-facing LLM prototypes into production by operationalizing prompts, hardening RAG pipelines, and adding monitoring/guardrails. Has hands-on experience debugging intermittent production failures under high traffic (vector store timeouts/empty retrieval) and implementing fail-safe behavior plus alerting. Also partners closely with sales in pilots/POCs, customizing demos with customer data and running side-by-side comparisons to drive adoption.”

“Healthcare-focused LLM engineer who deployed a production triage and clinical knowledge retrieval assistant using RAG and LangGraph-orchestrated multi-agent workflows. Emphasizes clinical safety and compliance with robust hallucination controls, HIPAA/PHI protections (tokenization, encryption, audit logging, zero-retention), and human-in-the-loop escalation; reports a 75% latency reduction in a healthcare agent system.”

“AI/LLM engineer who built a production intelligent support system using RAG over a vectorized documentation library, addressing real-world issues like lost-in-the-middle context failures and doc freshness via automated GitHub-driven re-embedding pipelines. Emphasizes rigorous agent evaluation (component/E2E/ops) and prefers lightweight, decoupled workflow automation using message brokers (Redis/RabbitMQ) over heavyweight orchestration frameworks.”

“Built a production multi-agent orchestration platform to automate healthcare claims and HR workflows, combining LangChain/CrewAI/AutoGPT with RAG (FAISS/Pinecone) and fine-tuned open-source LLMs (LLaMA/Mistral/Falcon) in private Azure ML environments to meet HIPAA requirements. Emphasizes rigorous agent evaluation/observability (trajectory eval, adversarial testing, LLM-as-judge, drift monitoring) and reports measurable outcomes including 35% faster claims processing and 40% fewer chatbot errors.”

“Has production deployment experience delivering computer-vision systems on AWS (Docker + S3) including a GDPR-focused face/license-plate obfuscation pipeline and a semantic-segmentation project aimed at reducing annotation time. Worked closely with DevOps and frontend teams and partnered with CEO/CMO to present an AI-driven annotation workflow to non-technical VC stakeholders.”

“Telecom (Verizon) AI/ML practitioner who built a production multimodal system that ingests messy customer issue reports (calls, chats, emails, screenshots, videos) and turns them into confidence-scored incident summaries with reproducible steps and evidence links. Also built KPI/alarm-to-ticket correlation to rank likely root-cause domains (RAN/Core/Transport), cutting triage from hours to minutes and improving MTTR.”

“Frontend engineer with hands-on experience revamping IPTV native TV apps (Android TV, Xfinity, LG, Samsung), building scalable, lazy-loaded home page and content experiences that remain stable as backend playlists change. Emphasizes component/modular architecture (including MVP on Android TV), strong quality practices (CI, unit tests, code reviews, manual device testing), and modern React+TypeScript state management using Zustand with clean separation of business logic from UI.”

“Software engineer/product-focused builder who delivered real-time supply chain inventory dashboards to replace a legacy system, integrating directly with ERP/WMS/TMS to eliminate manual reporting. Uses TypeScript/React with Redux Toolkit on the frontend and microservices + REST APIs on the backend, with performance improvements via Redis caching and a strong focus on user-feedback-driven prioritization and observability in distributed systems.”

“Frontend/platform-leaning engineer who owned a shared React component library (design-system style) adopted across multiple SPAs, with strong discipline around SemVer, deprecation strategy, and developer documentation. Also has backend/distributed-systems experience diagnosing and fixing a Kafka microservice race condition and has led prioritization/migration planning in an unstructured monolith-to-microservices environment (Centene).”

“Full Stack Developer (recently at Cisco Systems) building end-to-end web applications with Angular frontends and Spring Boot microservices backed by MySQL/JPA, including JWT + role-based access. Has hands-on experience with high-volume, real-time data processing/visualization and has solved complex UI state consistency issues using RxJS BehaviorSubjects; also applies layered state patterns in React with Redux Toolkit and uses AI dev tools (Cursor/Claude) strategically.”

“Full Stack Engineer with hands-on experience owning customer-facing products end-to-end, emphasizing fast iteration via feature flags and risk-based testing for critical user flows. Built TypeScript/React systems with shared types and clean backend layering, and has microservices experience using RabbitMQ to decouple services and manage scale issues like queue backlogs. Also created an internal dashboard for dev/QA to centralize build/test/deploy visibility and iterated on it through lightweight user research and usage metrics.”

“Full-stack engineer who owned end-to-end delivery of a customer-facing financial services web platform and built internal tooling for engineering teams. Strong in microservices and event-driven systems (Kafka/RabbitMQ), distributed transaction management (saga), and production performance/observability—achieving ~40% backend response-time improvement through database and query optimization.”

“Machine Learning Intern at Docsumo who delivered a customer-facing fraud-detection solution end-to-end: rebuilt the pipeline, deployed a Random Forest model, and shipped a Python/Flask microservice on AWS SageMaker. Drove measurable production impact (precision +30%, processing time cut in half, manual review -60%, customer satisfaction +15%) and demonstrated strong customer integration and live-incident response skills.”

“Graduate student researcher on the EARTH project (college collaboration with Moog) working on robotics for an arm/bucket system. Implemented waypoint-based path planning, built an Apriltag data pipeline, and developed ROS 2 tooling including a joystick-to-DeltaCAN teleop node; exploring reinforcement learning policies trained from Tera simulator + ROS 2 bag data to optimize trajectory planning under varying pressure/load conditions.”

“AI/ML engineer with forensic analytics and healthcare claims experience (Optum), building production LLM/RAG systems to surface context-driven fraud patterns from unstructured claim notes and explain risk to investigators. Strong in large-scale retrieval performance tuning, legacy API integration with reliability patterns (SQS, circuit breakers), and MLOps orchestration on Airflow/Kubernetes with rigorous testing, monitoring, and stakeholder-friendly interpretability.”

“Full-stack engineer with enterprise experience at Metasystems Inc. (and Qualcomm) building high-traffic, security-sensitive systems—owned a secure transaction processing module end-to-end using Java/Spring Boot, Python/Django, and React. Strong AWS production operations (EKS/ECS/Lambda/RDS/DynamoDB) with IaC (Terraform/CloudFormation), observability, and reliability patterns; also delivered resilient ETL/integration pipelines with idempotency/retries/backfills and achieved a 50% deployment-time reduction through CI/CD and modular refactoring.”