Vetted Machine Learning Professionals

“Robotics engineering intern and mechanical engineering master’s student who bridges hardware testing and ML/ROS2 software: built a PyTorch model to map motor test data across motor types using electrical specs (Kv/Kt/R/L) and validated it against new motors to meet strict torque/thermal accuracy targets. Also integrated CNN-based perception into ROS2 for real-time navigation and implemented MPC with time-synchronized multi-topic messaging to avoid stale-data control issues.”

Ritika Ghosh

Screened ReferencesStrong rec.

Junior Robotics & AI Engineer specializing in ROS2 autonomy and real-time computer vision

Dallas, US3y exp

“Robotics software engineer from Stanley Black & Decker’s autonomous team who built and deployed a ROS2-based model predictive control system for a commercial autonomous lawn mower, integrating real-time localization, Nav2 planning, and custom control under real-time constraints. Has hands-on field debugging experience (Foxglove, TF timing, covariance/noise tuning) to resolve issues that only appeared outside simulation, plus containerized deployment and CI/CD experience.”

Aaditya Voruganti

Screened ReferencesStrong rec.

Junior AI & Software Engineer specializing in robotics and ML infrastructure

2y exp

“Robotics engineer from UIUC’s Intelligent Motion Lab who led the perception stack for a humanoid robotic nurse, fusing camera/LiDAR/IMU on NVIDIA Jetson Orin for real-time localization and scene understanding across six robots. Deep expertise in ROS 2 and edge ML optimization (TensorRT, CUDA, zero-copy), delivering major latency/throughput gains (10 FPS to 22+ FPS) and building fault-tolerant pipelines with gRPC offloading and real-time reliability practices.”

“Healthcare solutions architect with roughly 7 years at Mass General Brigham, evolving from application specialist to broad cross-functional architect supporting telehealth, digital health, conversational AI, generative AI, and legal workflow initiatives. Particularly strong at translating complex clinical and business problems into enterprise architectures, integrations, and stakeholder-specific demos across hospital leadership, clinicians, IT, and vendors.”

“Backend/data engineer with strong authorization and compliance-domain experience: led a phased migration from a simplistic role model to modern RBAC on a Python serverless stack (Auth0 + AWS Lambda/API Gateway), coordinating changes across 5 repos with extensive manual and automated validation. Previously built and operated custom ETL pipelines (Airflow + Groovy/Java on Spark/YARN/Hadoop) to normalize messy customer email/chat/voice data for NLP-driven financial compliance indicators, including complex email journaling metadata enrichment and large-scale remediation reprocessing after production bugs.”

“Backend engineer who evolved the X-Ray gaming analytics platform, leading a zero-downtime MongoDB→AWS DocumentDB migration with dual-write, checksum-based validation, and Kubernetes canary rollouts while maintaining real-time monitoring for millions of concurrent sessions. Strong in FastAPI/Python API scaling and performance tuning (cut latency from ~2s to <150ms and reduced DB load 90%) plus production-grade auth/RLS security patterns (JWT, Supabase Auth, PostgreSQL RLS).”

“Engineering Manager/Tech Lead on Grindr’s monetization team who helped ship an AI-powered conversation summary feature (A-list), contributing across Android freemium implementation and backend LLM workflow service architecture/reviews. Demonstrated strong operational ownership by leading a Boost production incident from detection through rollback and prevention, and improved team throughput by introducing a lightweight end-to-end delivery process in a high-growth environment.”

Pavanika Thotakura

Screened

Senior Data Engineer specializing in cloud big data pipelines and real-time streaming

Seattle, WA6y exp

“Amazon data engineer who built a real-time fraud detection pipeline for AWS Lambda, tackling multi-region telemetry quality issues and scaling stream processing for billions of daily requests. Strong in production-grade data/ML workflows on AWS (EMR, Glue, Kinesis, SageMaker) with hands-on entity resolution and anomaly detection.”

Deekshit Myakala

Screened

Mid-level Software Engineer specializing in cloud automation and data/ETL platforms

Arlington, Virginia6y exp

“Backend engineer with AWS multi-region production experience building APIs and workflow automation for data center/storage hardware operations (firmware orchestration, maintenance checks, ticketing, dashboards). Also shipped an internal AI chat tool that parses hardware runbooks and incorporates user feedback to retrain the model, and has a strong testing/quality discipline (95%+ coverage) plus database performance tuning via indexing and query monitoring.”

Kevin Allen

Screened

Senior AI/ML Engineer specializing in conversational and generative AI

Austin, TX12y exp

“Built and productionized an LLM-based support assistant end-to-end, including RAG, APIs, monitoring, guardrails, and agent feedback loops. Stands out for translating GenAI prototypes into reliable production systems with structured evaluation, safety controls, and reusable Python infrastructure that improved both support quality and engineering velocity.”

“Research Extern at Google DeepMind and former AWS Software Development Engineer Intern with a strong focus on practical, trustworthy AI engineering. Built a multi-agent RAG system for personalized news headline generation using a fine-tuned Flan-T5 model, parallel critic agents, FAISS retrieval, and style embeddings, while also leading a 3-person team on the project.”

“Full-stack builder with hands-on experience shipping both location-based consumer products and AI-driven data platforms. Has owned end-to-end systems across React/Next.js, FastAPI, PostgreSQL, Streamlit, and geospatial tooling, with a strong emphasis on modular architecture, LLM reliability, and turning messy real-world data into usable product experiences.”

Andrew Bender

Screened

Executive product leader specializing in EdTech and SaaS

Cambridge, MA20y exp

“Product leader with deep edtech and AI experience who helped scale instaGrok from a disappointing launch to 330,000 monthly users in one school year without paid acquisition. Has operated as the sole product decision-maker in a tiny startup, consulted through founder-strategy conflict, and brings a human-centered view of AI as a tool for exploration and learning rather than replacement.”