Vetted Hugging Face Professionals

“ML/AI engineer with production experience across retail and healthcare: built a real-time computer-vision shelf monitoring system at Walmart and optimized edge inference latency by ~30% using TensorRT/ONNX and pruning. Also partnered with CVS Health clinical/pharmacy teams to deliver a medication-adherence predictive model, using Streamlit explainability dashboards and achieving an 18% adherence improvement.”

Wei Jiang

Screened

Junior Machine Learning Engineer specializing in MLOps and statistical modeling

Greenwood, SC3y exp

“Integration engineer at ES Foundry who led deployment of ELsentinel, a production EL image-based solar cell quality monitoring system using a Swin Transformer classifier (>0.8 F1 across 15+ classes) plus a live real-time prediction dashboard. Strong in solving messy labeling/data-quality problems with process-team collaboration and shipping ML systems despite limited compute/infrastructure.”

Utkarsh Mittal

Screened

Intern Data Scientist specializing in computer vision and LLM agents

Sunnyvale, CA0y exp

“Software engineering candidate with hands-on experience building and shipping LLM agents: created a production AI enrichment/coding agent at Covalent Metrology using Apollo.io + OpenAI, and built a Mistral hackathon router that dynamically selects among models to reduce token cost while maintaining quality. Also developed a real-time financial margin analysis agent that emails actionable insights and iterated on reliability issues (e.g., fixing misrouted emails, improving news relevance filtering).”

Vaibhav Sharma

Screened

Mid-level Software Engineer specializing in AI/ML and data platforms

Remote, USA5y exp

“AI/ML engineer who built a production agentic system to automate computational research experiments (simulation execution, parameter exploration, and numerical analysis) and mitigated context-window failures using constrained tool-calling/prompt-chaining patterns in LangChain with OpenAI tool-enabled models. Also has adtech/big-data pipeline experience at InMobi, orchestrating Spark jobs in Airflow to filter bot-like user IDs and publish clean IDs to an online NoSQL store for live serving, plus Apache open-source collaboration experience.”

Suloni Praveen

Screened

Entry-Level Software Engineer specializing in data engineering and ML systems

Los Angeles, CA0y exp

“Built an end-to-end Next.js/TypeScript LLM-based scientific PDF analyzer using local Ollama/Llama inference to prioritize privacy and cost, producing structured research artifacts (e.g., authors/methods/findings) with ~92% extraction accuracy. At Qualtrics, helped replace a batch pipeline with a real-time, low-latency ML inference service (Python/Go on Kubernetes) using Redis caching, Grafana-based observability, and graceful fallbacks to protect UX during failures.”

Atharva Bhide

Screened

Entry Software Engineer specializing in AI/ML and multimodal systems

Los Angeles, CA1y exp

“Built and shipped a production healthcare AI platform for a clinic in Brea, LA that combined LLM-based clinical report generation, voice agents for appointment workflows, and camera-based patient monitoring. Stands out for pairing multimodal AI architecture with production-grade reliability and compliance practices, while delivering concrete business results including 90% workflow automation, 200 hours saved per month, and a 60% improvement in customer retention.”

“Early-career analytics candidate with hands-on experience in SQL/Python data pipelines, Tableau reporting, and marketing engagement analytics across internship and startup settings. Stands out for combining rigorous data quality practices with practical AI system design, including an end-to-end GPT-4 grading capstone that emphasized explainability and human oversight.”

“AI/backend engineer who has owned production applied-ML systems end to end, including a Jitsi meeting intelligence platform with custom RoBERTa boundary detection, LLM summarization, and automated retraining from user feedback. Also has healthcare AI experience building a diabetes medication titration system with strict validation, drift monitoring, and safety guardrails—showing both product speed and high-stakes engineering rigor.”

“Product-minded full-stack engineer who has built AI-heavy systems spanning Next.js/TypeScript frontends, Python/FastAPI backends, queues, databases, and workflow infrastructure. Stands out for combining strong technical depth with UX instincts—improving trust in AI assistants, shipping ambiguous client features quickly, and creating reusable primitives for AI generation and analysis products.”

“Product-minded full-stack engineer with experience shipping engagement features and core communication systems at DribbleUp and Expys. Stands out for combining rapid MVP execution with rigorous iteration: delivered a leaderboard feature that lifted engagement by 8% initially and 20% overall, built a chat MVP in 3 days, and has hands-on experience deploying LangChain-based concierge agents with evals and human review.”

“ML/NLP engineer with experience at Flatiron Health building a production NLP platform that processed millions of clinical notes, using BERT/BiLSTM-CRF and spaCy to extract and normalize entities from noisy EMR text with oncologist-in-the-loop validation. Also built scalable retail ML workflows (Spark + Kubernetes + feature store caching) and applied vector databases plus contrastive-learning fine-tuning to improve retrieval relevance and recommendations.”

Pavan Kumar Malasani

Screened

Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and GenAI

Remote, USA4y exp

“GenAI/ML engineer in Citigroup’s finance environment who has deployed production RAG systems for investment banking under strict privacy and model-risk constraints. Built an internal-VPC Llama2 + Pinecone + LangChain solution with NER redaction and citation-based verification to prevent hallucinations, delivering major time savings, and also partnered with global finance executives to ship an AI early-warning indicator for treasury/liquidity risk.”

Anishkumar Mahalingam Iyer

Screened

Intern Software Engineer specializing in AI/ML infrastructure and applied machine learning

Palo Alto, CA2y exp

“Interned at Rivian where they built and deployed a production Whisper-based ASR + LLM real-time event labeling pipeline to help autonomous-vehicle engineers diagnose failures and route issues to triage teams. Also built a stateful multi-agent "Code Partner" developer assistant using LangGraph/LangChain (planner/router/coder/critique/tester) with evaluation, adversarial testing, and stakeholder-friendly communication practices.”

Krishna Kodur

Screened

Mid-level Robotics & AI Researcher specializing in human-robot interaction and reinforcement learning

Santa Clara, CA8y exp

“Robotics software engineer who built an end-to-end mobile manipulation platform (Franka Panda on a Clearpath Ridgeback) for a simulated-kitchen human-robot interaction study with natural speech commands, implemented in Python/ROS. Has hands-on experience integrating diverse sensors (RealSense, LiDAR, biosignals) with deep learning frameworks (PyTorch, Hugging Face) and fine-tuning GPT-Neo, plus simulation (Gazebo) and modern deployment practices (Docker/Kubernetes, CI/CD).”

Atyab Hakeem

Screened

Junior Data Scientist / ML Engineer specializing in GenAI and computer vision

San Francisco, CA2y exp

“Software engineer who built and deployed OddPulse, a multi-agent LLM-powered continuous financial auditing system aimed at reducing compliance penalties by catching issues before audit cycles. Experienced with TrueAI-based agent orchestration, Airflow on GCP batch workflows, and rigorous evaluation/benchmarking (hit rate/MRR, latency/TTFT, cost) alongside security controls for sensitive financial data.”

Abhay Ravi Kumar

Screened

Junior Software Engineer specializing in backend, cloud DevOps, and ML/NLP

Bengaluru, India1y exp

“DevOps/data-automation professional with HPE experience who has deployed containerized microservices to AWS EKS and built an end-to-end observability stack (Prometheus/Grafana/CloudWatch via Terraform), reporting zero-downtime deployments and ~40% faster incident response. Also extends Python ETL automation for procurement/operations teams (rules engine, validation, performance tuning) and bridges SAP ERP data into Power BI/Qlik dashboards through close on-site user collaboration.”

Pandari G

Screened

Mid-level Machine Learning Engineer specializing in Generative AI and RAG systems

San Francisco, USA5y exp

“GenAI/LLM engineer with production deployments in both fintech and retail: built an AI-powered mortgage document analysis/automated underwriting pipeline at Fannie Mae (OCR + custom LLM) cutting underwriting review from 3–4 hours to under an hour with privacy-by-design controls. Also helped build Sephora’s GenAI product advisory bot using LangChain-orchestrated RAG (Azure GPT-4, Azure AI Search, MySQL HeatWave vector search), focusing on grounding, evaluation, and compliance-aware architecture choices.”

Vamsi Reddy

Screened

Mid-level AI/ML Engineer specializing in healthcare and financial ML systems

Nashville, TN5y exp

“ML/AI engineer with hands-on experience shipping both predictive healthcare models and clinical GenAI assistants into production. They combine strong MLOps depth across Azure and AWS with healthcare-specific safety thinking, including PHI guardrails, retrieval grounding, and production monitoring, and they also built internal Python tooling for fraud ML workflows at Capital One.”

“ML/AI engineer with strong production depth across classical ML, MLOps, LLM/RAG, and scalable Python data platforms, with experience at Cisco and Accenture. Stands out for tying technical decisions to measurable business outcomes, including $1.2M annual savings, 40% faster support resolution, and broad internal adoption of shared engineering frameworks.”

Saumya Pailwan

Screened

Junior Software Engineer specializing in applied AI and audio ML

Evanston, IL2y exp

“Engineer with unusually mature experience leading AI-assisted development, including orchestrating multiple coding agents across a data pipeline feature as if managing a small engineering team. Stands out for balancing aggressive adoption of AI tools with disciplined judgment around architecture, security, and merge quality, and for translating that experience into stronger tech leadership.”

“Built and shipped a production LLM feature that converts natural-language search requests into Lucene queries for OpenSearch-backed device event data, improving usability for non-technical users. Brings hands-on experience across the full stack of agentic systems: model training, FastAPI/React integration, Kubernetes deployment on AWS, event-driven orchestration with NATS/Kafka, and production-grade evaluation/observability.”