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Vetted PyTorch Professionals
Pre-screened and vetted.
Yurong Luo
Senior Data Scientist/ML Engineer specializing in scalable ML and LLM systems
“Built and deployed an end-to-end product that brings a research-paper approach into production for large-scale time-series clustering, with attention to partitioning, latency, and scalability. Also designed a Python-based backend validation service (comparing outputs to database ground truths) and handled production reliability issues by reproducing dataset-specific crashes and hardening corner-case behavior with client-friendly errors.”
Chandan Chalumuri
Mid-level Data Scientist specializing in ML, NLP, and Generative AI
“Data engineering / ML practitioner with experience at MetLife building transformer-based sentiment analysis over large unstructured datasets and productionizing pipelines with Airflow/PySpark/Hadoop (reported 52% efficiency gain). Also implemented embedding-based semantic search using Pinecone/Weaviate to improve retrieval relevance and enable RAG for customer support and document matching use cases.”
“ML/NLP engineer with recent Scotiabank experience building production-grade indexing automation over large-scale emails and customer databases, combining LLM fine-tuning (Mistral, XLM-R) with fuzzy matching to exceed 95% accuracy under strict banking constraints. Also built a RAG-based chat agent using Gecko embeddings, Vertex AI Search, Gemini, and cross-encoder reranking, and delivered a text-to-SQL chatbot at SOTI through iterative fine-tuning and benchmark-driven experimentation.”
Sharanya Rao
Mid-level AI/ML Engineer specializing in NLP, LLMs, and RAG for finance and healthcare
“Built an AI lending assistant (RAG + DeBERTa) used by credit analysts to retrieve policies and past loan decisions, tackling real production issues like hallucinations, document quality, and sub-second latency. Deployed a modular, Dockerized AWS architecture (ECS/EMR + load balancer) with load testing, caching/precomputed embeddings, and CloudWatch monitoring, and used Airflow to automate scheduled data/embedding/vector DB refresh pipelines with retries and alerts.”
Asvad Shaik
Mid-level AI/ML Engineer specializing in Generative AI, RAG, and NLP
“Backend engineer who built and migrated a large-scale document intelligence platform used by legal, healthcare, and insurance clients, processing millions of pages. Experienced moving from a monolithic, LLM-heavy approach to a modular FastAPI service architecture with ML classification + RAG, strong validation/auditability, and enterprise security (JWT/OAuth, RBAC, PostgreSQL RLS) with zero-downtime incremental rollouts.”
Teage Johnson
Junior Full-Stack Engineer specializing in FinTech and machine learning
“Software engineer at early-stage startup Cari with hands-on experience shipping AI-enabled production workflows, including an LLM chatbot for a micro-transit platform and an automated image-processing pipeline integrated with Claude. Stands out for combining practical agent reliability patterns—schema validation, fallbacks, caching, and idempotency—with strong ML evaluation instincts and experience cleaning messy operational invoice data.”
Edward Ryan
Mid-level Full-Stack Engineer specializing in AI, automation, and synthetic data
“Full-stack product engineer who has owned complex internal platforms end-to-end, spanning React/TypeScript frontends, Flask/Redis backend systems, and relational data design. Particularly strong at turning technically dense workflows into intuitive user experiences, including a synthetic-imagery platform adopted by multiple Army research labs and a marketing analytics system with 99.99%+ uptime.”
Martin Wells
Executive AI & Technology leader specializing in AI, streaming, and gaming platforms
“Worked at an accelerator and served as an Entrepreneur in Residence, providing CTO and general founder support to startups. Although not pursuing entrepreneurship personally, they bring direct exposure to early-stage company building and accelerator ecosystems.”
Fernando Mosqueda
Senior AI/ML Engineer specializing in healthcare AI and MLOps
“Healthcare AI engineer with hands-on ownership of production ML and LLM systems at McKesson, spanning clinical risk prediction and RAG-based documentation tools. Stands out for combining deep clinical-data experience, HIPAA-aware deployment practices, and measurable impact through reduced readmissions, clinician workflow gains, and 20% to 30% faster ML delivery for engineering teams.”
Phani K
Mid-level AI/ML Engineer specializing in GenAI, NLP, and healthcare-financial ML
“ML/AI engineer with hands-on experience shipping healthcare AI systems, including an oncology risk prediction platform and RAG-based clinical decision support tools. Stands out for combining clinical domain context with strong production engineering across Spark, FastAPI, AWS SageMaker, monitoring, evaluation, and safety guardrails.”
Devang Borkar
Mid-level AI Engineer specializing in LLM agents and evaluation systems
“Built an end-to-end Python integration for an emotion-aware presentation feedback system that processed uploaded or live video and analyzed facial emotion, tone, and gestures. Also has Playwright automation experience in a loan management workflow, with emphasis on reliability, observability, security, and iterative delivery under ambiguous requirements.”
Pallavi Hogalagere Anandareddy
Mid Software Engineer specializing in backend systems and FinTech
“Backend/full-stack engineer with experience spanning banking implementations at FIS and hands-on product building in React, TypeScript, Node.js, SQL, and Python. Stands out for owning six bank go-lives end-to-end, building an AI resume evaluator, and proactively automating manual operations workflows to reduce errors and improve consistency.”
Shashidhar Alla
Mid-level Software Engineer specializing in full-stack cloud applications
“Backend-leaning full-stack engineer who has shipped both enterprise workflow software and AI-powered document intelligence products. Stands out for combining practical product judgment with strong production debugging skills across Spring Boot, GraphQL, FastAPI, vector search, and RAG systems, and for improving adoption by making AI search experiences intuitive for non-technical users.”
Raj Kalantri
Intern-level Software Engineer specializing in AI/ML and full-stack development
“Built a sophisticated AI career counselor as a full-stack web app for early-career students, integrating React, Flask, Pinecone, and LLM inference into a stateful conversational product. Stands out for combining hands-on debugging of retrieval/embedding pipelines with strong browser-performance instincts and pragmatic UX iteration based on real user testing.”
Shejal Shankar
Mid-level Full-Stack Engineer specializing in AI and real-time systems
“Full-stack engineer who shipped a production "Financial Insight" assistant dashboard in Next.js App Router/TypeScript, integrating a RAG pipeline (embeddings + ChromaDB + LLM) via route handlers and owning post-launch performance (latency, token cost, retrieval relevance). Also built/optimized Postgres-backed workflows for an outbound dialer and callback routing engine handling ~10,000 daily contacts, validating query performance with EXPLAIN (ANALYZE, BUFFERS).”
Sandeep Reddy
Mid-level Software Engineer specializing in full-stack cloud-native systems
“Full-stack engineer with hands-on experience building real-time analytics and logistics platforms across modern JavaScript and Java stacks. They combine strong production ownership and database optimization skills with architectural leadership, including redesigning bottlenecks with SQS/Lambda and driving a monolith-to-microservices migration on Kubernetes that cut deployment time by 50%.”
Sri Teja
Mid-level AI Engineer specializing in LLM systems and enterprise data platforms
“Built and owned key parts of Ripley, an AI-powered multi-agent operations platform for roadside assistance that automates high-volume customer service workflows at production scale. They designed the orchestration, evaluation, monitoring, and enterprise integrations, helping drive 70-80% automation and ~99% reliability across thousands of weekly interactions and millions of annual requests.”
Larry Scanniello
Mid-level Full-Stack Engineer specializing in AI and real-time audio applications
“Built WaveReel, a personal browser-based video chat app with a collaborative real-time digital audio workstation, pushing the browser beyond typical limits with OPFS, SharedArrayBuffers, and Audio Worklets. Demonstrates unusually deep front-end systems knowledge at the intersection of UI, real-time collaboration, and audio engineering.”
Anthony Jin
Junior Software Engineer specializing in full-stack, data, and AI systems
“Full-stack engineer who independently built and still operates a live e-commerce clothing site, owning everything from frontend UX to GraphQL backend, auth, and Stripe payments. Also helped ship an AI-powered survey follow-up product using GPT-4.1, with hands-on experience in prompt design, Lambda-based architecture, and pragmatic LLM integration in an early-stage startup environment.”
VISHESH BHARGAVA
Intern AI/ML Engineer specializing in financial NLP and data pipelines
“Full-stack and AI-oriented builder who has shipped both operational business software and experimental LLM systems. They owned a payment reconciliation dashboard that automated UPI payment matching and dramatically reduced manual effort, and also built a financial signal platform using FinBERT, knowledge graphs, Gemini, and backtesting with strong guardrails and human oversight.”
SrujanaSwathi Botcha
Mid-level Product Manager specializing in enterprise platforms and healthcare technology
“Product manager with 4 years of PM experience and an earlier software engineering foundation, spanning enterprise healthcare, operations platforms, and AI products. Most notably, they founded and shipped a GenAI app builder SaaS, built evaluation and reliability layers for production use, and exceeded first-quarter adoption targets by 35%.”
Nishchal Gante
Mid-level Data Scientist specializing in MLOps and Generative AI
“Robotics software/ML engineer who built perception and navigation-related ML systems for autonomous supermarket carts, including object detection, shelf recognition, and obstacle avoidance. Strong ROS/ROS2 practitioner who optimized real-time performance (reported 50% latency reduction) and deployed containerized ROS/ML pipelines at scale using Docker, Kubernetes, and CI/CD.”
Alfred Fox
Senior AI/ML & Full-Stack Engineer specializing in GenAI, RAG, and MLOps platforms
“Backend/data platform engineer who owned end-to-end production services for a fleet analytics/GenAI platform, spanning FastAPI microservices on Kubernetes and AWS (EKS + Lambda) event-driven workloads. Strong in reliability/observability (OpenTelemetry, circuit breakers, idempotency), data pipelines (Glue/Airflow/Snowflake), and measurable performance/cost wins (SQL 10s to <800ms P95; ~30% compute cost reduction).”