Vetted Computer Vision Professionals

YERTAYEV ARMAN

Screened

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

Seoul, South Korea

“Robotics/SLAM-focused engineer who worked on RT-Appearance mapping using NetVLAD, replacing traditional CV feature extraction with a deep learning approach to improve loop closure in repetitive green environments. Has hands-on ROS1/ROS2 experience (including bridging), point-cloud alignment with G-ICP for sensor-parameter matching, and Gazebo+Docker simulation testing for motion planning/perception.”

“Software engineer with hands-on experience across platform modernization, production AI agents, and workflow automation. They led a monolith-to-microservices migration that increased deployment speed from weekly to daily, built a self-healing GPT-powered browser agent with an 85% autonomous recovery rate, and founded/ran ZapDash, where they hardened Kafka-based integrations against silent data loss.”

“Software/product engineer who has owned a consumer iOS dating app from customer discovery through roadmap execution, while also shipping an in-app LLM-powered support/feedback bot. Brings a mix of product sense and backend systems experience, including rebuilding a race-condition-prone event orchestration system and designing microservices to handle arbitrary black-box production data.”

“Senior backend/data engineer who has built and maintained HIPAA-compliant, real-time clinical FastAPI services on AWS, orchestrating ML/LLM and vector DB calls with strong reliability patterns (auth, timeouts/retries, graceful degradation, idempotency). Also delivered AWS IaC/CI-CD (Terraform/Helm/GitHub Actions) across EKS/Lambda/SageMaker and built Glue/Spark ETL with schema evolution and data quality controls, plus demonstrated large SQL performance wins (15 min to <9 sec) and hands-on incident ownership.”

“New grad who shipped an LLM-powered writing app (“Write-it”) to production on Azure with CI/CD (GitHub Actions + JFrog) and implemented an unconventional RAG pipeline to prevent repetitive prompts using embeddings and cosine similarity. Also participated in a Luma AI image/video generation hackathon, iterating with artist feedback and improving usability by rewriting non-technical prompts via an LLM.”

“Unity developer with broad cross-platform experience (HTC Vive VR, iOS/Android, Nintendo Switch, WebGL) who built a near-solo multiplayer AR iOS party game using Photon/PUN and AR Foundation. Demonstrates strong practical networking and performance optimization skills, including lag compensation and client-side shader-based rendering strategies.”

“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.”

“Sales professional at AutoSherpa who partners closely with product/engineering to resolve CRM data-sync issues affecting pipeline accuracy and sensitive customer data handling. Experienced running multiple customer onboardings in parallel using structured tracking (CRM/project boards), early risk logging, and weekly stakeholder syncs; targeting $100k base and open to equity.”

Ajay Kudapa

Screened

Mid-level Software Engineer specializing in Healthcare IT and Web3

4y exp

“Software engineer with experience spanning healthcare middleware (patient records + insurance integration) and an AI fantasy football product built with React/TypeScript, Firebase, API gateways, and pandas-based data pipelines. Has hands-on microservices scaling experience (latency mitigation, async migration, state-based redesign) and built an internal feature-toggle dashboard that improved demo efficiency and sales outcomes.”

Ritika Sisodiya

Screened

Junior Data Scientist specializing in statistical modeling and machine learning

California, USA2y exp

“AI Researcher with production experience building a real-time computer-vision detection pipeline augmented by an LLM-based verification layer to cut false positives (~78%) and reach ~90% real-world accuracy. Also partners cross-functionally with Product/Sales/Marketing to shape AI feature prioritization and market positioning using analysis and interactive dashboards.”

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.”

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.”

Deeptanshu Patel

Screened

Intern Software Engineer specializing in LLM security and automation

Plano, TX0y exp

“Built and shipped a production LLM safety-testing agent using Gemini with support for running multiple LLMs, focused on fast (<5s) execution and clear failure-mode reporting. The tool helps smaller companies evaluate whether their LLMs respond safely by grading outputs and tuning scoring consistency.”

Vishnu Priyan Sellam Shanmugavel

Screened

Mid-Level Applied AI Engineer specializing in LLM services, RAG, and OCR/NLP extraction

Arlington, VA4y exp

“Backend/platform engineer who built and evolved a large-scale healthcare document processing system (OCR + LLM orchestration) in Python/FastAPI on Google Cloud (Cloud Run, GCS, Firestore), processing ~1.5M files per batch and tens of millions overall. Emphasizes reliability and operational safety via deterministic IDs, idempotent state machines, strong observability, and self-healing reconciliation, plus disciplined migrations using dual-run validation and incremental rollouts.”

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.”

“Lead XR Developer for Fortune 500 training simulators who owned the VR portion end-to-end in Unity/C#, including a patented VR display algorithm (VR NOW). Built a device-agnostic OpenXR architecture with headset-aware rendering fallback and integrated hardware like D-Box motion platforms; reports VR release drove higher sales and improved trainee engagement and performance.”

“Analytics professional with experience spanning infrastructure, energy, and digital engagement data. They have built SQL and Python workflows to turn messy operational data into trusted reporting assets, and led a wind turbine SCADA analysis that quantified roughly $1M in cumulative performance loss and translated findings into actionable Power BI dashboards.”

“Candidate has hands-on hardware experience fabricating a MIMO UWB antenna, including identifying mathematical design parameters, using simulation software, and validating performance through S-parameter results. Also mentions using Figma for design work, though details on workflow and collaboration were limited.”

“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.”

“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.”

“Robotics engineer focused on simulation, modeling, and control, with hands-on sim-to-real experience from a soft, foldable “grasshopper” robot where friction/contact physics and servo dynamics drove real-world performance gaps. Built a ROS 2 voice-operated TurtleBot system integrating YOLOv5 + stereo depth for object picking with an attached arm, and debugged AMCL/SLAM to cut localization error from 10–13 cm to ~5 cm. Currently developing a quadruped in MuJoCo with a 3-layer control stack (RL + MPC + PD) and an RL training pipeline in JAX ahead of hardware.”