Vetted Linux Professionals

Utkarsh Gupta

Screened

Junior Robotics & ML Engineer specializing in perception, navigation, and VLA models

Los Angeles, CA1y exp

“Robotics software engineer with hands-on AGV/AMR experience at ERIC Robotics, building ROS2-based LiDAR perception and localization on NVIDIA Jetson for real-time deployment. Improved unstable localization in challenging environments (e.g., tunnels/bushes along rail tracks) via scan-matching, filtering, and consistency checks, and cut latency by moving from rclpy to rclcpp and leveraging CUDA. Comfortable across the stack from simulation (MuJoCo/Isaac Sim/Gazebo, domain randomization) to deployment tooling (Docker, basic CI) and distributed ROS2/DDS systems.”

Jhansi D

Screened

Senior Full-Stack Java Developer specializing in cloud-native microservices

Chicago, Illinois4y exp

“Full-stack engineer with experience building secure, cloud-native document/workflow platforms handling high-volume customer and medical data across microservices on Kubernetes. Demonstrated impact improving performance via event-driven AWS architectures (Lambda + DynamoDB Streams) and strengthening compliance/security for S3-stored documents using IAM and KMS. Has delivered end-to-end APIs and UIs using Java/Spring Boot with Angular/React, plus Docker and CI/CD.”

Aditya Agrawal

Screened

Intern Software Engineer specializing in backend systems and data engineering

1y exp

“Backend/AI engineer who has built and shipped two products: Know Founder (Python/SQL/AWS) scaling to 2,000+ users in the first month, and Unifr (unifr.online), an AI search visibility engine that queries multiple LLMs and turns responses into structured brand insights. Strong in production reliability/performance (Redis caching, indexing, precomputation) and in designing agentic workflows with guardrails, validation, retries, and human escalation.”

Mohan Naik Megavath

Screened

Mid-level Data Engineer specializing in real-time pipelines and cloud data platforms

Remote, USA4y exp

“Backend engineer with hands-on experience building secure Python/Flask services (sessions, JWT, RBAC) and optimizing PostgreSQL/SQLAlchemy performance, including custom SQL using CTEs/window functions profiled via EXPLAIN ANALYZE. Also integrates LLM features via OpenAI/Azure into backend systems and improves scalability with RabbitMQ-driven async processing, caching, and multi-tenant data isolation patterns.”

Jeongeun Lee

Screened

Mid-level AI/Robotics Engineer specializing in autonomous systems and perception

San Diego, CA4y exp

“Robotics software engineer in an Autonomous Vehicle Lab building an end-to-end ROS 2 autonomous golf cart stack (sensor integration, SLAM, planning, and camera+LiDAR perception). Demonstrated strong systems-level debugging by fixing a FastLIO2 LiDAR timestamp/IMU-window issue that restored mapping quality, and stabilized real-time GigE camera perception by diagnosing backpressure and tuning ROS 2 QoS plus compressed transport.”

“Data/streaming engineer in capital markets who led an end-to-end trade settlement data product (Kafka→MongoDB→data lake) with rigorous data-quality logic and ~$175K first-year operational impact. Also built a low-latency Go-based CME market data engine feeding SOFR curve generation, using MSK on EKS with performance tuning (idempotency, compression, partitioning) to achieve sub-100ms delivery.”

“Built production LLM agents at Hivenue and Amazon, spanning consumer booking automation and internal data-query/reporting workflows. Stands out for combining conversational UX with strong reliability engineering—strict tool use, state machines, schema validation, idempotency, and evaluation pipelines—and can point to measurable impact including a 21% reduction in time to book and a 12% conversion lift.”

“Built an LLM-powered customer segmentation agent during a Chewy internship, consolidating Snowflake data into a knowledge graph so non-technical marketing users could query customer cohorts in natural language. Stands out for combining agent/tooling design with rigorous data engineering practices, including schema audits, imputation, validation layers, and idempotent pipelines on messy large-scale datasets.”

“SRE-oriented infrastructure engineer who built an internal Vertex AI/Gemini knowledge chatbot to centralize product and development documentation, cutting routine support questions from 10+ daily to roughly 2. Also brings hands-on experience debugging Kubernetes production incidents and monitoring ETL/data-quality issues in Dataflow-based pipelines.”

“Backend engineer with experience spanning regulated financial systems at Oracle Financial Services and early-stage product building at Apli.ai. Has owned production onboarding infrastructure end-to-end, improved reliability through strong observability and incident response, and also built AI-backed backend workflows using AWS, Bedrock, and RAG.”

“Backend/AI infrastructure engineer focused on production-ready LLM systems and distributed workflows. They described building a RAG-based multi-step agent with strong reliability controls, evaluation loops, and graceful degradation that improved latency by 30%, retrieval accuracy by 15%, and reduced support workload by 40%.”

“AI-focused full-stack product builder from Verizon Applied Research who has shipped internal tools spanning API documentation governance, patent exploration agents, and prompt optimization. Particularly strong at turning unreliable or opaque LLM behavior into structured, trustworthy product workflows that enterprise users can actually adopt.”

Joseph Selby

Screened

Senior Product Leader specializing in EdTech and digital assessment

Remote, USA17y exp

“Edtech product leader with deep experience across Pearson, SMART, and Promethean, spanning legacy platform modernization, classroom engagement tools, and early AI in education. Particularly compelling for roles at the intersection of product, pedagogy, and human-centered AI: they rebuilt SMART's 29-year-old Notebook platform and have repeatedly designed products around real teacher and student needs.”

“Built Taskline end-to-end as a solo founder: an AI-native operations platform for tradespeople with web and mobile apps, AI receptionists, invoicing, scheduling, and payments. Particularly interesting for teams seeking a zero-to-one full-stack builder who can turn LLM/agent capabilities into practical, low-friction products for non-technical users.”

“Worked at AMD on Vivado tool releases, focusing on adding and integrating new IP/SoC functionality into the Vivado flow and validating expected behavior through testing. Comfortable engaging with customers and open to travel for hands-on customer-facing work.”

“Runs two lean real estate companies remotely by building local on-the-ground contact networks and leveraging free-tier technology to keep total annual business costs under $100. Brings a cost-elimination and MVP/validation-first mindset, preferring to join an established company unless a clearly viable business idea emerges.”

“Backend engineer who built an NL-to-SQL system at Target, using a multi-step LLM pipeline with vector-store schema retrieval and SQL validation to safely answer business questions. Strong in production FastAPI systems (async, Pydantic, Docker/Uvicorn, load balancing) and security (OAuth2/JWT, scopes, and database row-level security), with experience migrating Flask apps to FastAPI + PostgreSQL using strangler/feature-flagged canary rollouts.”

“Master’s robotics candidate at Northeastern (Silicon Synapse Lab) who built and tuned an NMPC for the M4 multi-modal morphobot to achieve high-speed (>10 m/s) aggressive flight maneuvers and even hover under a full rotor failure, using MATLAB/CasADi/Simulink/Simscape with IPOPT. Also has ROS/ROS 2 experience spanning SLAM/navigation on a UGV and GPS/IMU sensor-fusion + dead-reckoning with custom ROS 2 nodes/messages, with a strong simulation-first and real-time debugging approach.”

“AI/LLM engineer who built a production autonomous GenAI content ecosystem that generates short-form scripts, extracts viral highlights from long-form video, and dubs content into 33+ languages. Focused on making LLM outputs production-safe via schema enforcement, token-to-time alignment, critic-agent verification, and scalable async orchestration—cutting manual workflows by ~90% and saving $200k+ annually.”

“Backend/data engineer with production experience building secure Django/DRF APIs (JWT RS256 + rotating refresh tokens), background processing with Celery, and strong reliability practices (timeouts, retries/backoff, structured logging, audit trails). Has delivered AWS solutions spanning Lambda + ECS with IaC/CI-CD and built Glue/PySpark ETL pipelines with schema evolution and data-quality quarantine patterns; also modernized a legacy SAS pipeline to Python/PySpark with parallel-run parity validation and phased rollout.”

“Built and shipped a production multi-agent conversational AI platform (Monitor agent + RAG + 4 additional agents) with enterprise REST APIs, using ChromaDB-grounded WCAG knowledge to keep responses accurate while varying tone via personality modes and conversation memory. Has experience at LinkedIn delivering technical demos and pre-sales guidance to both engineering teams and C-level stakeholders, acting as a translator between sales and technical teams to drive adoption.”

“AI/LLM engineer who built a production content-generation system for nursing education, combining multimodal RAG over proprietary PDFs (including images) with structured Cosmos DB data and external sources. Strong focus on production reliability—prompt-chaining with LangChain, validation/guardrails, and Azure-based monitoring/observability—plus experience designing Azure AI agents with tool integrations like Bing Search.”

“Built and deployed a production long-form article summarization system using BART/T5/PEGASUS, tackling real-world constraints like token limits, latency/quality tradeoffs, and factual drift via chunking/merge logic and constrained decoding. Uses pragmatic Python-based pipeline orchestration (scheduled jobs, modular scripts, logging/retries) and iterates with stakeholder feedback to make outputs genuinely useful for content workflows.”

“Backend/distributed-systems engineer (Amazon; also Bank of America) pivoting into robotics software. Built and owned an end-to-end cross-region event processing service for Aurora Global Databases, emphasizing correctness under latency/clock skew, fault tolerance, and strong observability; brings deep Docker/Kubernetes and CI/CD experience to robotics infrastructure and reliability work while ramping up on ROS 2.”