Vetted Hugging Face Professionals

“CTO-level technology leader with experience building end-to-end tech strategy and roadmaps, modernizing legacy environments in healthcare (GenesisCare), and scaling engineering into large global teams (Amadeus). Built a DevOps organization at Syniverse for the Visibility Suite, implementing Kubernetes/Terraform/Chef automation that drove ~75% faster deployments, and is known for staying hands-on (including data center work) while leading strategically.”

“Engineer with a notably mature AI-native development process: uses Claude/Claude Code in a test-first, iterative workflow and has led multi-agent builds across frontend, backend, and testing. Most notably, they led development of an AI voice agent platform, creating custom agent skills and enforcing clear architectural boundaries to deliver a stable, scalable system.”

Alexander Smith

Screened

Junior Software Engineer and Data Scientist specializing in AI/ML systems

California, USA3y exp

“Built production-grade automation and ML/data pipelines at Dun & Bradstreet and ThreadNotion, spanning large-scale document classification, country risk report automation, and resilient Playwright testing for dynamic AI chat workflows. Particularly strong in turning brittle or ambiguous systems into reliable, observable, end-to-end automated platforms.”

Tara Munjuluri

Screened

Junior Software Engineer specializing in full-stack and AI systems

Ames, IA3y exp

“Backend-focused engineer with end-to-end ownership experience on internal platforms at John Deere, including a workforce and skills system that cut manual review time by 40%. Brings a strong reliability and compliance mindset across Java/Python microservices, AWS infrastructure, and production operations, and has also built an LLM-powered RAG system over 1M+ records with emphasis on grounded outputs and observability.”

“Second most active contributor at Daice Labs, owning a production AI-powered software development collaboration platform’s end-to-end execution infrastructure (TypeScript/Next.js backend, Node.js CLI, shared libs). Built the full multi-agent pipeline (planning/codegen/summary), Supabase-backed context assembly and realtime state, Git/GitHub automation, and a provider-agnostic LLM abstraction with strict Zod validation and retries, backed by extensive tests and design specs.”

“Engineering leader focused on large-scale enterprise SaaS and MDM platforms, with experience modernizing monoliths into microservices, improving reliability, and scaling systems to support 15M devices across AWS and Azure. Stands out for combining deep platform architecture work with strong org-building: managed teams up to 45 globally and built a 0-to-1 platform services team to 22 people in under a year.”

Darsh Sharma

Screened

Mid-level Software Engineer specializing in ML systems and microservices

Madison, WI2y exp

“Teradata Text Security intern who built a production LLM-powered planner agent that decomposes complex tasks into dependency-aware subtasks (DAG/topological graph) and executes them via a custom orchestrator with parallelism, status tracking, and error handling. Also contributed to an HR-facing internal document chatbot concept to streamline onboarding, showing cross-functional collaboration.”

“Robotics/ML engineer with hands-on experience building multimodal waypoint prediction for autonomous driving using CLIP + LidarCLIP embeddings and PyTorch, including nuScenes data pipelines and baseline modeling. Also built ROS 2 nodes for TurtleBot maze navigation with an image-classification pipeline, and has Caterpillar experience doing dSPACE HIL testing with MATLAB/Simulink plant models for engine software validation.”

Pranav Puranik

Screened

Senior AI Engineer specializing in LLMs, RAG, and multimodal NLP

Austin, TX5y exp

“Built a production LLM/RAG assistant for insurance/health claims agents that ingests 100–200 page patient PDFs via OCR (migrated from local Tesseract to Azure Document Intelligence) and delivers grounded claim detail retrieval plus summaries with PII/PHI guardrails. Experienced orchestrating large workflows with Celery worker pipelines and AWS Step Functions (S3-triggered, Fargate-based batch inference/accuracy aggregation), and collaborates closely with non-technical SMEs (claims agents/nurses) through shadowing, iterative demos, and SME-defined evaluation.”

“Product-minded software engineer with experience shipping AI-powered financial insights (spend forecasting, cashflow, credit optimization) and building real-time analytics systems using React/TypeScript and FastAPI. Has designed microservices with RabbitMQ/gRPC and strong observability (Prometheus/Grafana/OpenTelemetry), and also built an internal Figma plugin adopted by designers that reduced export time by 50%.”

“Built and shipped production improvements to a Paylocity RAG-based AI assistant, redesigning retrieval into a hybrid HNSW + keyword pipeline and using tuned RRF to fuse rankings—cutting latency by ~2s and reducing token usage by ~5000. Previously spearheaded Apache Airflow integration across ETL pipelines at Acuity Knowledge Partners, creating reusable templates and automated triggers to reduce manual job monitoring.”

“ML/GenAI engineer with hands-on experience shipping LLM-powered support systems at Uber, including real-time feedback analysis, ticket summarization, and retrieval-grounded knowledge systems. Stands out for combining fine-tuning, RAG, safety evaluation, and production optimization to drive measurable support outcomes like faster handling times, better resolution rates, and lower latency/cost.”

“Postdoctoral/PhD researcher focused on Alzheimer's disease who has built scalable end-to-end AI systems across pathology imaging, transcriptomics, and longitudinal neuroimaging. Particularly strong in turning novel research ideas into robust Python pipelines on GPU/HPC infrastructure, with pathologist-facing impact and scientifically novel findings such as image-derived disease severity trajectories and subtype discovery.”