Vetted Hugging Face Transformers Professionals

Kruti Mehta

Screened ReferencesStrong rec.

Mid-level Software Engineer specializing in backend systems and FinTech analytics

Maryland, USA5y exp

“Engineer with a pragmatic, high-leverage approach to AI-assisted development: uses AI and multi-agent workflows aggressively for implementation and internal tooling, while maintaining strict human oversight for user-facing features. Stands out for treating agents like junior engineers, breaking work into actionable tasks, and combining robust testing, local E2E validation, and feature-flag rollouts to safely ship production code.”

“ML/NLP practitioner who has delivered production systems in regulated domains, including a healthcare compliance pipeline using RAG (GPT-4/Claude) plus TF-IDF retrieval that increased document review throughput 4.5x. Also has hands-on experience improving fraud detection data quality via entity resolution (Levenshtein, Dedupe.py) validated with A/B testing, and building scalable, monitored workflows with Airflow, CI/CD, and AWS SageMaker.”

“Built and deployed a production LLM-powered text-to-SQL/document intelligence chatbot on AWS that lets non-technical business users query complex enterprise databases in plain English. Demonstrates deep practical expertise in schema-aware prompting, embeddings-based schema retrieval, SQL safety/validation guardrails, and rigorous offline/online evaluation with human-in-the-loop approvals for risky queries.”

“Built a production-grade LLM evaluation and regression system that stress-tests models across hundreds of iterations, combining LLM-as-judge, semantic similarity, statistical metrics, and rule-based checks, with results delivered via stakeholder-friendly HTML reports and dashboards. Experienced orchestrating multi-agent RAG workflows using LangChain/LangGraph and event-driven GenAI pipelines in n8n integrating OCR, speech-to-text, and external APIs, with strong emphasis on reliability, observability, and explainable failures.”

“Built and deployed an LLM product called "Content Craft" combining BART-based summarization with a RAG Q&A chatbot using LangChain, embeddings, and a vector database. Has hands-on MLOps experience containerizing and serving models with FastAPI and running them on Kubernetes with monitoring, self-healing, and autoscaling, and has practical experience reducing hallucinations through structured prompting.”

“Software engineer who deployed and stabilized a real-time analytics platform at Senecio Software, focusing on production reliability, observability, and performance under load. Experienced debugging issues spanning distributed services and networking (e.g., tracing timeouts to packet loss from misconfiguration) and extending Python (FastAPI/Django) APIs for customer-specific analytics features in a configurable, maintainable way.”

“Full-stack engineer who has delivered production customer analytics/dashboard features using Next.js App Router + TypeScript on the frontend and Java Spring Boot microservices on the backend. Demonstrates strong production ownership (monitoring latency/error rates/adoption) plus hands-on performance work across React rendering and Postgres query/index optimization, and has implemented Temporal-like durable workflows with retries and idempotency.”

“Independent builder of production-grade systems: shipped an end-to-end URL shortener with JWT auth, Redis rate limiting/caching, Postgres, Docker, and real-time analytics, and separately architected a Redis-backed distributed task queue handling 1000+ tasks/min. Demonstrates strong distributed-systems instincts (atomicity, retries/DLQ, idempotency, heartbeats) plus a focus on maintainable code and self-documenting APIs (FastAPI/OpenAPI, versioned routes).”

“Built GynAI, an end-to-end maternal clinical decision support platform for OB/GYN practices and hospitals in North America, combining predictive ML with RAG-based LLM explainability. The candidate emphasizes real production ownership across experimentation, deployment, monitoring, and iteration, with reported impact including fewer delayed interventions in high-risk pregnancies and a 15-20% reduction in false positives.”

“Backend/AI engineer who built Matchable, an end-to-end AI-powered workforce matching platform using FastAPI, transformer-based NLP, PostgreSQL, and AWS, with a strong focus on practical system design tradeoffs. Also brings research-oriented experience from Los Alamos/ASU simulation work and has built multi-agent LLM workflows with schema validation and auditability, suggesting a thoughtful approach to reliability in AI systems.”

Homak Patel

Screened

Junior Software Engineer specializing in Agentic AI and Data Systems

2y exp

“Forward Deployed Engineer at EasyBee AI who productionized a self-storage customer’s multi-agent LLM system end-to-end—rebuilding it with LangGraph/CrewAI, integrating with real property management + CRM systems via an MCP server, and adding observability/guardrails for reliable daily use. Experienced in live troubleshooting of agentic workflows, developer demos/workshops (including an open-source project, MerryQuery), and partnering with sales to close deals through customer-specific technical demos and fast integration feedback loops.”

Sree Manasa Vuppu

Screened

Mid-level AI Engineer specializing in Generative AI, LLMs, and RAG

Charlotte, NC5y exp

“Internship at Discovery Education building a production LLM/RAG chatbot that let marketing and sales teams query and interpret Looker/BI dashboards in natural language, with responses grounded in compliance and state education standards. Emphasizes rigorous evaluation (faithfulness/precision/recall/latency) plus user-feedback analytics, and used LangChain for orchestration, chunking/context-window control, and integration with enterprise sources like SharePoint.”

“Built and deployed real-world systems end-to-end across security and healthcare contexts: led a 3-person team delivering a university vehicle tracking system with 30% cost savings and 1-year post-launch monitoring. Also implemented a healthcare RAG chatbot with adaptive query routing that cut LLM costs by 40% while maintaining answer accuracy, and has experience debugging non-deterministic LLM behavior in DevOps pipeline automation.”

“LLM/agent systems builder who deployed a production hybrid router for immerso.ai that dynamically selects retrieval vs reasoning vs generative pathways, achieving an 82% factual-accuracy lift. Deep hands-on experience optimizing local Mistral 7B inference (4–5 bit GGUF quantization, KV-cache reuse) and building reliable RAG/agent workflows with LangChain/LangGraph/AutoGen across GCP Cloud Run and AWS (ECS/Lambda).”

“Built and deployed a production generative-AI copilot at Tungsten that automates invoice/form extraction template creation, reducing weeks of manual model-building work. Combines fine-tuned LLMs (PyTorch/HuggingFace) with OpenCV layout grounding to reduce hallucinations, and runs an end-to-end Kubeflow-based MLOps pipeline with drift monitoring, canary releases, and automated retraining.”

“LLM engineer who has deployed a production RAG system (LangChain/FAISS/FastAPI) for enterprise semantic search, tackling real-world latency by LoRA/PEFT fine-tuning and grounding outputs with retrieval. Brings strong MLOps (Docker, AWS EKS, CI/CD, MLflow) plus stakeholder-facing explainability experience using SHAP to align ML-driven financial guidance with non-technical domain experts.”

MANOGNA VADLAMUDI

Screened

Junior AI Engineer specializing in LLM evaluation, prompt engineering, and AI orchestration

Chicago, IL1y exp

“LLM workflow builder who has deployed a personalized GPT experience (including Delphi AI-based knowledge ingestion) and built a LangChain/LangGraph job-aggregation pipeline that ingests, normalizes/dedupes, filters, then uses an LLM to rank and summarize matches. Emphasizes production reliability with structured outputs, retries/fallbacks, metric-driven evaluation, logging/prompt versioning, and A/B testing, and collaborates with non-technical stakeholders through demo-driven iteration.”

Varun Mahankali

Screened

Junior Full-Stack Software Engineer specializing in React, Node.js, AWS, and Generative AI

3y exp

“Built and production-deployed a Streamlit-based PDF RAG chatbot using LangChain (FAISS, embeddings, prompt templates) and OpenAI, optimizing Streamlit’s stateless behavior by caching vector DB + chat history to cut latency and API cost. Demonstrates a rigorous evaluation mindset (gold datasets, unit tests, LLM-as-judge, groundedness KPIs) and has experience communicating privacy/accuracy safeguards (RBAC, data masking, citations) to a non-technical client at Kalven Technologies.”

Binaya Sharma

Screened

Senior Software Engineer specializing in full-stack systems, big data, and applied AI

Baton Rouge, LA6y exp

“Built and deployed ForensicLLM, a local domain-specific LLaMA-3.1-8B model for digital forensic investigators using RAFT + RAG over 1000+ curated research papers, with citation-aware responses and rigorous evaluation (BERTScore/G-Eval). Deployed via vLLM and Docker and validated through a chatbot survey with 80+ participants; published at DFRWS EU 2025.”

Karan Baid

Screened

Intern Machine Learning Engineer specializing in Generative AI and RAG systems

Jaipur, India

“Early-career AI/LLM builder who created and deployed a multi-agent news analysis agent (Patrakarita) using CrewAI, coordinating researcher/analyst roles to turn noisy article URLs into structured, prioritized outputs (claims, tone, verification questions, opposing views). Strong focus on orchestration debugging and reliability evaluation, including measuring hallucination/redundancy and improving reasoning by refactoring pipeline sequencing.”

“Software/product engineer who has owned a consumer iOS dating app from customer discovery through roadmap execution, while also shipping an in-app LLM-powered support/feedback bot. Brings a mix of product sense and backend systems experience, including rebuilding a race-condition-prone event orchestration system and designing microservices to handle arbitrary black-box production data.”

“Union College robotics graduate with hands-on ROS experience building an independent “waiter bot” that took spoken orders (Mozilla DeepSpeech), used OpenCV, and navigated via SLAM on a TurtleBot with LiDAR. Also led the Union College Robotics crew for four years and implemented PID control for micromouse maze robots; has additional software deployment experience with Dockerized FastAPI and CI/CD from coursework.”