Vetted PyTorch Professionals

“Internship experience building a production LLM-powered podcast operations agent that automated lead intake (HubSpot), guest research, scheduling (Calendly), meeting-summary evaluation (Gemini), and human approval via Slack bot—while retaining rejected candidates for future outreach. Also contributed to ideation of a multi-agent orchestration framework with parsing and task routing, and emphasized reliability via structured prompts, HITL feedback, and prompt-based test sets.”

“LLM engineer who has deployed privacy-preserving, real-time workplace risk monitoring over massive enterprise chat/email streams, tackling latency, hallucinations, and extreme class imbalance with model benchmarking, RAG + fine-tuning, and a pre-filter alerting layer. Also built an agentic legal contract drafting system (Jurisagent) using LangGraph/LangChain with deterministic multi-agent control flow, structured outputs, and reliability-focused evaluation/telemetry.”

“Software engineer who owned customer-facing, high-traffic TypeScript/React + TypeScript backend systems end-to-end, emphasizing safe velocity through feature flags, staged rollouts, observability, and rollback-ready incremental delivery. Reports shipping more frequently with fewer production incidents and faster recovery due to these guardrails.”

“Built and deployed a production healthcare medical review agent that automates call-transcript summarization and medication reconciliation using a hybrid deterministic + LangGraph-orchestrated LLM workflow. Demonstrates strong reliability engineering (guardrails, schema validation, confidence thresholds, golden/adversarial eval, Langfuse monitoring) in a regulated environment, delivering 60% lower latency and 70%+ efficiency gains while partnering closely with care managers and operations.”

“Built and shipped a production LLM-powered investor research feature for a fintech product, focused on grounded answers and minimizing hallucinations. Implemented retrieval-quality and evidence-coverage gating with clear refusal fallbacks, and evaluates systems with regression tests and metrics like correct-refusal rate, hallucination rate, and latency. Comfortable orchestrating workflows with LangChain or custom Python depending on production needs.”

“AI/LLM engineer at Capital One who deployed a production RAG-powered fraud analysis and document intelligence platform using LangChain, OpenAI, Pinecone, Kafka, and AWS. Focused on reliability in real-time investigations via hybrid retrieval, schema-validated outputs, and LLM verification loops, reporting review-time reduction from hours to minutes and ~99% fraud detection precision.”

“Built and productionized a RAG-based analytics Q&A assistant for a financial analytics team, enabling natural-language querying across 200+ datasets (SQL tables, PDFs, compliance docs, wikis) and cutting turnaround time by 60%. Deep experience delivering regulated, audit-ready LLM systems on Azure (Azure OpenAI + LangChain) with strict grounding/citations, hybrid retrieval, and AKS-based low-latency deployment, plus strong collaboration with compliance analysts and auditors via iterative Gradio demos.”

“Built and deployed a production RAG-based multi-agent chatbot during an internship to help consultants answer client questions and guide users through new IT systems with step-by-step instructions. Demonstrates hands-on experience with LangGraph/LangChain/Google ADK, unstructured document parsing and chunking for RAG, and a reliability-first approach to agent workflows (metrics, fallbacks, human-in-the-loop, guardrails).”

“ML/LLM engineer building production, real-time fraud detection for financial transactions using a two-tier architecture (fast ML + GPT) to deliver both low-latency decisions and analyst-friendly risk explanations. Experienced orchestrating end-to-end retraining, drift monitoring, and automated model promotion with Databricks Jobs/Workflows and MLflow, and partnering closely with fraud analysts to tune alerts, thresholds, and dashboards.”

“ML/LLM practitioner with production experience building a healthcare review sentiment pipeline (RateMDs) using Hugging Face Transformers plus a LangChain+FAISS RAG layer for interactive querying. Also led orchestration-driven optimization of Nike’s Fusion ETL pipeline, improving runtime efficiency by 20%, and has experience translating ML outputs into Tableau dashboards for non-technical healthcare stakeholders (e.g., readmission risk).”

“Built an "AskMyVideo" system that turns YouTube videos into queryable knowledge graphs by transcribing audio (Whisper), chunking and embedding content, and enabling traceable answers back to exact timestamps. Strong in entity resolution (rules + fuzzy matching + TF-IDF/cosine with PR-curve thresholding) and modern retrieval stacks (FAISS, hybrid dense/sparse, domain fine-tuning with ~12% precision gain), with a production mindset using Airflow/Prefect, Docker/FastAPI, and LangSmith/Prometheus/Grafana observability.”

“Backend/ML-systems engineer with experience (including Amazon) building real-time face recognition services using PyTorch (MTCNN/FaceNet) and AWS (SQS/S3/Lambda/EC2) with a focus on low latency, burst handling, and cost control. Also led a revenue-critical legacy pricing workflow migration to a serverless event-driven architecture using strangler-pattern rollout, simulation-based validation, and strong security practices (JWT/RBAC/RLS).”

“At Citi, helped lead the productionization of an internal LLM-driven automation workflow into a production-ready developer platform, focusing on determinism/reproducibility, security, and cost controls. Implemented prompt versioning/registry, JSON schema validation, sanitization, and deep telemetry (including manual edit-distance) plus human-in-the-loop review and phased rollout—driving major SDLC efficiency gains (e.g., test script creation cut from ~1 week to ~1 day).”

Shanmukha Koganti

Screened

Mid-level AI/ML Engineer specializing in recommender systems and edge computer vision

Bay Area, CA6y exp

“ML/AI engineer with production experience at Shopify and Intel, building a deep learning product ranking system that lifted add-to-cart ~14% and serving real-time similarity search via FAISS+Redis under <20ms latency at massive scale. Also deployed computer vision models to 100+ retail edge locations using Docker/Ansible/k3s with zero-downtime rollouts, and applies strong MLOps practices (A/B testing, canary/shadow, observability) plus performance optimization (OpenVINO, INT8).”

Amit Sharma

Screened

Principal Software Engineer specializing in AI/LLM platforms, payments, and healthcare systems

San Francisco, CA25y exp

“Engineering player-coach who recently shipped an agent-based workflow to extract key info from unstructured web data (browser agents + CDP) and populate daily digests/calendars, owning architecture through testing. Also built a Flask-based LLM evaluation and regression testing system using G-Eval/Confident AI dashboards, and applies a rigorous, research-driven approach to selecting third-party tools with stakeholder buy-in; has healthcare ops/onboarding workflow experience at Vivio Health.”

Sai Dinesh Pusapati

Screened

Senior AI/ML Engineer specializing in GenAI agents and LLM workflows

San Francisco, CA6y exp

“LLM/AI engineer with production experience building a retrieval-based document intelligence system that extracts information from PDFs/emails, backed by Python + Spark pipelines. Focused on reliability and cost/latency optimization (caching, batch processing) and has hands-on orchestration experience with Airflow (sensors, retries, alerts). Also partnered with business stakeholders to deliver customer feedback classification/summarization for faster sentiment insights.”

Atulya Bist

Screened

Junior Data Scientist / Software Engineer specializing in LLM analytics and robotics

Los Angeles, CA3y exp

“Robotics/ML engineer who implemented TD3 and PPO in PyTorch to solve the challenging OpenAI Gymnasium humanoid-v5 MuJoCo task, including custom networks, rollout logic, and training scripts. Also has hands-on robotics coursework experience with ROS-based RRT motion planning on a real robotic arm, plus practical CI/CD and containerization experience (Docker, Jenkins, GitHub Actions). Currently exploring world models (VAE + sequence generator) using Euro Truck Simulator data.”

“ML/AI engineer with Goldman Sachs experience building production fraud detection and RAG-based trading insights systems end-to-end. Stands out for combining real-time ML infrastructure, GenAI retrieval systems, and compliance-aware design, with measurable impact including nearly 25% false-positive reduction and improved analyst productivity.”

“Built and owned end-to-end production systems for a healthcare platform, including a predictive task recommendation feature (React + FastAPI + ML on AWS ECS) that cut backlog 20% and saved coordinators ~10 hours/week. Also productionized an AI-native RAG system (vector DB + LLM) delivering 40% faster query resolution, and led phased modernization of a monolithic FastAPI service into async microservices using feature flags and canary releases.”

Sai Karthik Chittamuru

Screened

Senior Salesforce Developer specializing in AI systems and enterprise cloud solutions

Pittsburgh, PA15y exp

“Salesforce-focused engineer with hands-on experience building Sales Cloud and Service Cloud solutions, including a Zoho billing integration for quote/contract workflows and a multi-panel LWC case management dashboard. Stands out for making practical architecture decisions around middleware vs. custom REST, handling idempotency with upsert patterns, and modernizing legacy Aura patterns with Lightning Message Service.”

“Built production-grade Python systems in a medical/imaging context, including an image feature extraction and survival prediction microservice with strong testing, validation, and observability practices. Also developed a Playwright-based autonomous job application agent that handled dynamic UIs and anti-bot challenges with stealth tooling, proxies, and human-in-the-loop escalation.”

Srinivas Vasudevan

Screened

Junior Software Engineer specializing in distributed systems and FinTech

Durham, NC3y exp

“Built an end-to-end payment fraud monitoring dashboard with a React/TypeScript frontend, GraphQL backend, Redis hot path, and a production RAG chatbot, while solving real-time latency and scaling issues. Also shipped an OCR system on AWS EKS for a live manufacturing line at Troxler, improving production accuracy by 15% with custom preprocessing and model tuning.”

“Backend-focused AI engineer with enterprise experience building startup-style internal products at JPMorgan Chase. He helped create an AI-powered financial research platform for analysts, leading retrieval and multi-agent orchestration work that cut research prep from hours to under 20 minutes while scaling across large volumes of SEC filings and earnings transcripts.”