Vetted PyTorch Professionals

Avni Tripathi

Screened ReferencesModerate rec.

Mid-level Data Scientist specializing in NLP, RAG, and information retrieval for RegTech

Gurgaon, India5y exp

“Built and deployed a production document Q&A/research platform that combines semantic search (vector DB embeddings) with structured knowledge-graph querying to reduce analyst research time. Used in high-stakes domains like Politically Exposed Person profiling and extracting critical information from ESG/regulatory documents, with a human-in-the-loop evaluation process (precision@k and source-text highlighting) to ensure accuracy.”

“AI/backend-focused builder who has shipped two distinct applied AI products: a game discovery platform with vector search + RAG chat, and an AI accounting platform for small businesses. Stands out for combining product discovery with hands-on system design, including sub-100ms retrieval performance, privacy-conscious financial workflows, and measurable impact like 58% compute-time reduction and support for 24,000+ user profiles.”

Affan Ahmed

Screened ReferencesModerate rec.

Senior Full-Stack & Machine Learning Engineer specializing in scalable SaaS and cloud AI

Remote, Pakistan7y exp

“Frontend engineer who led an enterprise self-serve analytics dashboard end-to-end using a micro-frontend React/TypeScript architecture with strong integration discipline (contracts, CI gates, ADRs). Demonstrated measurable performance wins (35% faster LCP) through code splitting, lazy loading, and tighter Redux subscriptions, and uses feature flags plus automated E2E coverage for controlled rollouts.”

“Built and shipped a production LLM hallucination detection and monitoring pipeline using semantic-level entropy (embedding-clustered multi-generation variance) to flag unreliable outputs in downstream automation. Implemented a scalable async architecture (FastAPI + Docker + Redis/Celery) with strong observability (structured logs + PostgreSQL) and developed evaluation loops combining controlled prompts and human review; also partnered with non-technical stakeholders on AI-driven form validation/document processing.”

“Built and productionized a real-time social-media sentiment analysis system used by a marketing team to monitor brand/campaign performance. Experienced in orchestrating LLM workflows with LangChain (validation → prompting → parsing → post-processing), plus monitoring, retraining, and RAG-style retrieval using embeddings/vector stores to keep outputs reliable over time.”

“AI engineer who built an agentic, production-deployed LLM workflow for tobacco violation parsing and automated multi-case creation, using six specialized agents and a human-in-the-loop confidence-threshold routing design. Addressed data privacy constraints by generating synthetic datasets with LLM prompting, and orchestrated reproducible end-to-end pipelines in LangChain with robust testing and evaluation (precision/recall, micro-F1).”

“Backend/platform engineer who built an LLM-driven QA automation system (“mockmouse”) using a Flask orchestration microservice, Socket.IO real-time updates, Redis caching, and strict Pydantic schemas to turn prompts into reliable action graphs and automated browser tests. Has hands-on Kubernetes delivery experience (Docker/Helm/Jenkins) and has supported large migration programs, validating ETL cutovers with 1M+ synthetic records and rigorous output comparisons; also built event-driven monitoring/anomaly detection streaming into Grafana.”

“Backend/AI engineer with production experience in legal tech: built a high-scale licensing/subscription API (FastAPI/Postgres/Stripe) and shipped a RAG-based chatbot for an eDiscovery platform. Designed a robust legal document ingestion workflow that processes thousands of documents into a searchable vector index with clear retry/escalation logic, and has demonstrated measurable Postgres performance wins (200ms to 10ms) using EXPLAIN ANALYZE and composite indexing.”

“ML practitioner who built a production customer risk scoring system to replace slow manual approvals, owning the full pipeline from feature engineering and XGBoost training to deploying a Dockerized FastAPI prediction service. Emphasizes reliability and business-aligned evaluation (recall/ROC-AUC, threshold tuning, drift monitoring) and is comfortable translating model decisions into stakeholder metrics like conversion rate (experience at EasyBee AI).”

Prasad Sadineni

Screened

Mid-level AI Engineer specializing in LLM fine-tuning, RAG, and agentic systems

Nashville, TN6y exp

“Building and deploying production in-house, domain-specific LLM chatbots for enterprises that cannot use third-party GPT tools due to internal policies. Focused on reducing latency and improving domain awareness using fine-tuning, continual learning, and advanced RAG/agent retrieval strategies, with experience orchestrating multi-agent workflows via LangChain/LlamaIndex and vector DBs (FAISS, Weaviate, Chroma).”

Dhairya Shah

Screened

Entry-level Machine Learning Engineer specializing in computer vision and systems

Buffalo, NY1y exp

“ML-focused builder who has shipped an end-to-end income-class prediction product: built the data pipeline, trained models, deployed via Streamlit with a live UI, and tracked success via accuracy (84%), adoption, and latency. Demonstrates strong practical MLOps instincts (Docker/Streamlit Cloud, logging/monitoring, caching) and data engineering reliability patterns (schema checks, idempotency, retries, backfills) while iterating quickly in ambiguous, solo-project environments.”