Vetted C Professionals

Wilson Harron

Screened

Director-level AI/ML & Computer Vision Engineer specializing in robotics and multimodal AI

Los Angeles, CA15y exp

“Candidate is not currently pursuing entrepreneurship (no business plan and no capital raised) and is not familiar with the VC/accelerator landscape. They show pragmatic, problem-first thinking about evaluating startup ideas—prioritizing real customer pain points and the quality of the founding team—and are open to working for others rather than founding "at all costs."”

“Built production LLM/RAG systems during a UPS internship, including a shipment knowledge agent used across 15+ hubs worldwide and a multi-agent PDF RAG workflow. Stands out for combining hands-on enterprise integration with rigorous evaluation, hallucination reduction, and efficient fine-tuning techniques like LoRA.”

“AI Engineer with hands-on ownership of a production multi-agent RAG platform in financial services, spanning experimentation, architecture, deployment, monitoring, and iterative optimization. Stands out for measurable impact: 35% retrieval relevance improvement and nearly 50% reduction in manual operational analysis effort, plus strong experience making enterprise LLM systems safer and more reliable in production.”

“Full-stack developer who uses AI thoughtfully as a productivity multiplier rather than a substitute for engineering judgment. Built a stock search platform with React, Node.js, and MongoDB, and has experimented with multi-agent workflows across frontend, backend, debugging, and documentation while keeping rigorous human review over logic, testing, and maintainability.”

“Backend/AI engineer who has owned production agentic systems end-to-end, including a CRM-integrated multi-agent financial workflow at Wow Payments that cut latency by 83% and achieved 98% uptime. Also built an AI real estate product ('Site IQ') by turning vague stakeholder goals into a geospatial autonomous agent using RAG, rapid prototyping, and tight validation layers around GPT-4 outputs.”

“Frontend-focused engineer with experience delivering a major licensing-model migration, moving customers from legacy key-based licensing to a dynamic renewal experience. Strongest signals are in React/TypeScript architecture, reusable component design, micro frontends, and frontend quality/performance practices.”

Sai Divya Mulukala

Screened

Mid-level Full-Stack Software Engineer specializing in FinTech and distributed systems

USA5y exp

“Full-stack engineer with experience building operational dashboards at Walmart and improving digital banking experiences at Bank of America. Stands out for tracing performance issues across frontend, APIs, and backend services, including cutting response times from 1.2s to 700ms and resolving duplicate event-processing problems in distributed systems.”

“Technical director and Unity engineer with 7+ years shipping products, including Symphoni, a mixed reality rhythm game launched across Meta Quest and Pico headsets. Particularly compelling for teams needing someone who can architect complex gameplay-adjacent systems end-to-end—such as a robust UGC level editor shipped on a 3-4 week timeline—while also bringing VR optimization, multiplayer networking, and emerging AI-assisted development workflow expertise.”

“Robotics software engineer with semiconductor wafer-handling automation experience at Mattson Technology, building C++ control software for ATM and vacuum robots coordinating through a load-lock (pressure/door/pump sequencing). Implemented multi-vendor communications with auto-detection across SECS serial and TCP/IP, and validated behavior using simulation + GUI animation, logging/sniffers, and hardware-in-the-loop testing; currently self-studying ROS 2.”

“Backend engineer who built an async FastAPI data pipeline at GHN Career Academy to replace a manual Excel-based workflow, migrating 30k+ contact records into Airtable with validation/deduplication and best-effort GPT-based enrichment. Emphasizes reliability under messy real-world data and partial failures via structured logging, retries, and resumable processing, unlocking downstream automations (e.g., Zapier and chatbots).”

“Backend/data engineer who builds production FastAPI microservices and AWS serverless/Glue pipelines for SMS analytics and marketing segmentation. Led a legacy batch modernization into modular services (FastAPI + Glue/Athena + ClickHouse) using shadow-mode parity checks, feature flags, and incremental rollout. Demonstrated measurable performance wins (12s to sub-second SQL; ~40% CPU reduction) and strong incident ownership with proactive schema-drift prevention.”

“AI engineer who has shipped production LLM systems across customer service and marketing use cases—building a RAG app on Azure OpenAI and speeding retrieval with Redis caching tied to Okta sessions. Also implemented a LangGraph multi-agent workflow that pulls image context from Figma to generate structured HTML marketing emails, adding a verification agent to improve image-selection accuracy while optimizing solution cost for business stakeholders.”

“Robotics/ROS2-focused early-career engineer who built a stereo visual-odometry SLAM system for autonomous navigation and optimized it to run reliably in real time on Raspberry Pi. Strong in sensor fusion (camera+IMU), ROS2 debugging/profiling, and distributed robotics/IoT pipelines (ROS2 + MQTT + cloud), with added experience extracting WiFi CSI for sensing/localization and shipping via Docker + GitHub Actions CI/CD.”

“Built a web-based online Sudoku game in JavaScript (multiplayer format supporting up to 6 teams with up to 5 players each) and demonstrates strong product/analytics orientation. Uses a KPI-driven approach (DAU/WAU, ARPU, session duration, LTV) and structured prioritization methods (MoSCoW, story mapping, cost of delay, DFV) to iterate toward targets; seeking a remote role around $70k/year.”

“Backend/platform engineer who owned a real-time customer analytics microservice stack in Python/FastAPI with Kafka streaming into PostgreSQL, including schema enforcement (Avro) and high-throughput optimizations. Strong Kubernetes + GitOps practitioner (EKS/GKE, Helm, Argo CD) who has handled CI/CD reliability issues with automated pre-deploy checks and rollbacks, and supported major migrations (on-prem to AWS; VM to EKS) with blue-green cutover planning.”

“Backend engineer with hands-on manufacturing/production-systems experience at Wallbox, improving the Supernova charger rework process by streamlining part-number/component updates. Strong in building modular Python/Flask services with clean integration layers (Cosmos DB, NetSuite, traceability/label printing), plus deep SQLAlchemy/Postgres performance tuning. Also brings scalable AI/ML integration and deployment experience (OpenAI/Hugging Face/TensorFlow Serving, Docker/FastAPI/Nginx) and multi-tenant schema isolation with RBAC.”

“Full-stack developer who built an end-to-end Hotel Management System using React and Spring Boot with MongoDB and AWS. Has hands-on experience debugging API/data-fetching issues with Postman and validating results against the database, plus exposure to handling large data workloads with chunking and monitoring via Grafana/Tabula.”

“QA tester from core banking systems (SIT/UAT) looking to transition into console game testing; brings disciplined end-to-end testing, defect triage, and clear bug reproduction/documentation. Not yet familiar with Sony TRCs/Microsoft XR/Nintendo LOT checks but has a concrete plan to learn platform certification standards through documentation, mentorship, and hands-on practice.”

“Robotics software engineer who built a lightweight, ROS-free distributed control and telemetry stack for a Caltrans long-range culvert inspection robot. Strong in integrating heterogeneous hardware (UART motor controllers, Ethernet sensors, MJPEG cameras) and delivering real-time operator data via FastAPI/WebSockets, including reverse-engineering undocumented protocols and debugging network-induced latency with control-loop redesign.”

“QA engineer intern experience at Amazon (Alexa Daily Essentials) owning end-to-end quality for AI-powered timer/stopwatch features at massive scale. Demonstrates disciplined Jira-based workflow, automation-driven regression coverage, and strong device-matrix verification (Echo Show generations), with concrete examples of finding and driving resolution of complex UI/backend synchronization bugs.”

“Backend/API engineer with ~4 years experience building production services in .NET Core/PostgreSQL/Redis/Docker and optimizing real-world latency issues (claims ~60% response-time improvement). Also built and owned an end-to-end RAG-based AI assistant using Python/FastAPI, OpenAI APIs, and Pinecone, plus agentic workflows with reliability guardrails (retries, confidence thresholds, monitoring). Currently pursuing a master’s degree and targeting a $150k base salary.”

“Built and productionized LLM-driven content intelligence/SEO agents for a high-traffic media platform, automating tagging/summarization/metadata with FastAPI + async orchestration and strict JSON-schema outputs. Demonstrated measurable impact (40% faster publishing, +20% organic traffic in 3 months) and strong reliability practices (offline evals, shadow mode, canaries, fallbacks, idempotency, and monitoring).”

Yuvraj Singh Chauhan

Screened

Entry-level AI/ML Engineer specializing in LLMs, RAG, and DevOps automation

Bangalore, India1y exp

“Built and owned a production-scale AI-driven software release/version intelligence platform orchestrated via GitHub Actions that tracks 1000+ upstream repositories and automatically generates SLA-bound JIRA upgrade tickets for hardened container images. Replaced brittle regex/PEP440 parsing with an LLM-based semantic filtering layer plus deterministic validation to handle noisy/inconsistent GitHub tags at scale, with monitoring for coverage, latency, and correctness validated against upstream ground truth.”

Sujith Julakanti

Screened

Junior MLOps Engineer specializing in LLMs and cloud infrastructure

College Station, TX3y exp

“Built a production multimodal LLM system (Gemini on GCP) to automate behavioral coding of family-involved science experiment videos, including preprocessing for inconsistent lighting/audio and LangGraph-orchestrated parallel workflows. Also developed rubric-based AI grading workflows and partnered closely with non-technical education stakeholders through explainability-focused walkthroughs and manual-vs-AI evaluation alignment.”