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Vetted Neural Networks Professionals
Pre-screened and vetted.
Mid-level Machine Learning Engineer specializing in LLMs, RAG, and real-time recommendation systems
Senior Data Scientist specializing in AI/ML platforms for finance and healthcare
Mid-level Machine Learning Engineer specializing in Bayesian inference and reinforcement learning
Mid-level Machine Learning Engineer specializing in fraud detection and recommendations
Mid AI/ML Engineer specializing in LLM alignment and scalable AI systems
Ritika Ghosh
Junior Robotics & AI Engineer specializing in ROS2 autonomy and real-time computer vision
“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.”
Arnav Singh
Junior Software Engineer specializing in full-stack web, cloud data, and applied ML
“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).”
“Software engineer with experience at Amazon and Agora building end-to-end systems: a knowledge-base AI chatbot (React/TypeScript UI + retrieval/response backend + Docker deployment) and an internal approval governance platform using AWS Step Functions and DynamoDB. Emphasizes fast iteration without sacrificing trust via feature-flag rollouts, citation-required answers, abstention on low-confidence retrieval, regression query sets, and strong observability (request IDs, structured logs, latency/error monitoring).”
Likhitha Bethi
Mid-level Software Engineer specializing in backend systems, distributed systems, and applied AI
“Goldman Sachs engineer who owned end-to-end features for an internal onboarding and case management platform, spanning React/TypeScript UI, a GraphQL gateway, and Node + Spring WebFlux microservices. Built and operated a Kafka-based ingestion and search pipeline with DLQs, retries, idempotency, and strong observability, and improved developer experience via backward-compatible GraphQL API design and schema-driven documentation.”
Param Yanamandra
Staff/Lead Software Architect specializing in Contact Center platforms and GenAI automation
“Built and deployed production LLM systems in healthcare and at LinkedIn: automated pen-and-paper clinical trial evaluations with a 40x efficiency gain and created an evidence-based Evaluation Agent focused on accuracy and speed. Also used Temporal to orchestrate resilient data-ingestion workflows for customer support staffing prediction, improving prediction outcomes by 40% while handling missing data, retries, and backfills.”
Asrith Velireddy
Mid-level AI/ML Engineer specializing in MLOps, LLMs, and scalable ML systems
“ML/LLM engineer at Adobe who deployed a transformer-based personalization and campaign-targeting recommender system end-to-end, including PySpark/Airflow pipelines processing 12M+ events/day and containerized inference on AWS SageMaker (Docker/Kubernetes). Also has hands-on LLM workflow experience (RAG, semantic search, prompt optimization, hallucination mitigation) with a metrics-driven approach to reliability, drift monitoring, and reproducible retraining via MLflow.”
Kella Dhanush Venkata Sai
Junior ML Engineer specializing in Generative AI and LLM applications
“Built a production internal knowledge assistant using a RAG pipeline over large spreadsheets, PDFs, and support documents, using transformer embeddings stored in FAISS. Focused on real-world production challenges—format normalization, retrieval quality, hallucination reduction (context-only + citations), and latency—using hybrid retrieval, quantization, and containerized deployment, and communicated the workflow to non-technical stakeholders using simple analogies.”
Yongyan Cao
Principal Vehicle Dynamics & Control Systems Engineer specializing in autonomous driving and hybrid powertrains
“Robotics controls engineer with experience spanning an RV/trailer automatic hitching and towing robot (vision + EKF sensor fusion, anti-jackknife/anti-sway, multi-loop torque assistance control) and 3 years on a ROS-based RoboTaxi autonomous driving stack at Pegasus Technology. Improved MPC trajectory generation robustness by converting hard constraints to soft constraints with slack variables, and built an AI-powered PR review agent (Claude-code) integrated into CI/CD to reduce bugs.”
Senior Mechanical/Systems Engineer specializing in automation, additive manufacturing, and defense
Mid-level Full-Stack Software Engineer specializing in streaming media and real-time systems
Mid-level Data Scientist specializing in LLMs, RAG, and personalization
Intern Data Scientist specializing in LLMs, RAG, and data engineering
Mid-level Data Scientist specializing in GenAI, NLP, and deep learning
Mid-level AI/ML Engineer specializing in RAG systems and cloud data platforms
Junior Machine Learning Engineer specializing in NLP and computer vision
Mid-level Machine Learning Engineer specializing in NLP, federated learning, and fraud detection
Mid-level Data Scientist specializing in GenAI, NLP, and deep learning