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Vetted Hugging Face Professionals
Pre-screened and vetted.
Mid-level Data Science & AI/ML Engineer specializing in MLOps, NLP, and computer vision
Mid-level Data Scientist specializing in ML, NLP, and LLM-powered analytics
Mid-Level Software Development Engineer specializing in cloud platforms, IAM, and secure GenAI
Junior Machine Learning Engineer specializing in healthcare AI and GenAI RAG
Senior Unity & Full-Stack Engineer specializing in VR/XR and multiplayer systems
Mid-level AI/ML Engineer specializing in FinTech and production ML systems
Shreevershith Kollabettu
Mid-level AI Engineer specializing in LLMs, agentic systems, and MLOps
“AI-focused engineer with Infosys experience building Azure/.NET chatbot applications and recent hands-on work with FastAPI/LangChain. Built a hackathon multi-agent legal counsel system showcasing agent orchestration, and emphasizes production readiness via Docker, GitHub Actions CI/CD, pytest automation, and adversarial simulations for auditable AI behavior. No direct robotics/ROS experience to date.”
sai anuragh Sangoju
Mid-level AI/ML Engineer specializing in fraud detection, credit risk, and NLP
“Built and deployed a production LLM-powered university support chatbot on Azure using a RAG pipeline, focusing on reducing hallucinations, improving latency, and handling ambiguous queries via confidence checks and clarification prompts. Also has hands-on orchestration experience (Airflow/Azure Data Factory), including hardening a demand-forecasting ingestion workflow with sensors, retries, and automated alerts, and uses a metrics-driven testing/monitoring approach for reliable AI agents.”
Yashwanth Penugonda
Mid-level AI/ML Engineer specializing in LLMs, RAG, and production GenAI systems
“Built and deployed a production LLM-powered RAG knowledge system to unify operational/policy information across PDFs, wikis, and databases, emphasizing auditability and low-latency/cost performance. Improved answer relevance at scale by moving from pure vector search to hybrid retrieval with metadata filtering and reranking, and partnered closely with healthcare operations/compliance to define acceptance criteria and human-in-the-loop guardrails.”
Anisha Roseline Milton Fernandes
Mid-level Software Engineer specializing in FinTech and AI/ML
“Full-stack engineer with payments/settlement domain experience who modernized a payment tracking workflow from REST to GraphQL and delivered a production payment status dashboard using Next.js App Router + TypeScript. Strong in performance and reliability work (Postgres indexing/Explain Analyze, Redis caching, Datadog observability) and in durable event-driven processing with Kafka (DLQs, idempotency, reconciliation, event replay).”
Mohammed Saalim Kartapillai
Senior Backend/Full-Stack Engineer specializing in data platforms and cloud microservices
“Backend engineer who built and shipped an end-to-end AI outreach product (LazyMails) combining a LinkedIn-scraping Chrome extension with a FastAPI/Postgres backend and Gemini-powered email generation, achieving major personal productivity gains. Also has enterprise experience at TCS on Humana’s 500k+ user wellness platform running Kubernetes microservices with Azure DevOps CI/CD, plus Kafka-based real-time eligibility event streaming and GitOps-driven operations.”
Senior Backend Engineer specializing in cloud-native microservices and AI integrations
Senior Software Engineer specializing in backend systems, cloud infrastructure, and applied AI/ML
Mid-level Data Scientist & AI Engineer specializing in NLP, computer vision, and MLOps
Mid-level Data Engineer specializing in AI/ML, streaming, and lakehouse architectures
Principal Cloud & Data Architect specializing in AI-enabled AWS platforms
Senior Full-Stack Engineer specializing in Python and AWS-native application development
Atharva Pandkar
Junior AI/ML Engineer specializing in LLM agents and RAG systems
“Built and deployed a production, multi-tenant modular agentic AI platform at Easybee AI, using LangChain/LangGraph with Redis-backed durable state to make agents reusable, traceable, and auditable. Emphasizes reliability via strict tool schemas, deterministic controllers, tenant-level policy enforcement, and regression testing derived from real production failures; also delivered AI automation for legal/finance workflows (attorney draw and expense automation) with explainable, deterministic payouts.”