Vetted PyTorch Professionals

jiayu Zhao

Screened

Junior Quantitative Analyst and Full-Stack Engineer specializing in FinTech and web platforms

Chicago, IL6y exp

“Backend/distributed-systems engineer with AI infrastructure experience who built an AI-driven video generation platform, focusing on an asynchronous FastAPI-based orchestration layer between user APIs and heavy inference services. Strong in production instrumentation and latency/concurrency optimization; actively learning ROS 2 but has not yet worked on physical robotics or ROS-based deployments.”

Ahmed Atallah

Screened

Senior Controls & Localization Engineer specializing in autonomy, sensor fusion, and MPC

Troy, Michigan12y exp

“Robotics software engineer focused on state estimation and localization reliability, with deep hands-on EKF tuning/validation using DGPS ground truth and integrity-risk-based uncertainty calibration. Built middleware-agnostic interfaces with ROS wrappers to enable repeatable ROS bag playback testing, and implemented CI at Caterpillar to automatically build the localization stack and run unit tests plus bag-based regressions before merge.”

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.”

Shram Kadia

Screened

Mid-level Software Engineer specializing in backend systems, cloud-native apps, and AI platforms

Santa Clara, CA4y exp

“Backend/full-stack engineer who has owned production systems end-to-end, including a Dockerized Node.js/TypeScript probabilistic fault-tree analysis service for nuclear safety research deployed on AWS. Also built and operated a FastAPI-based RAG pipeline over 200+ PDFs using FAISS, focusing on low-latency, idempotent workflows and strong observability; experienced with API design and Playwright E2E automation across React/Angular projects.”

Mathew Martin

Screened

Mid-level Software Engineer specializing in cloud data platforms and LLM applications

New York, USA4y exp

“LLM/agent builder with experience shipping production LLM features at an early-stage ed-tech mental wellness startup (conversation analysis + structured feedback via FastAPI, OpenAI API, Render, CI/CD). Also built a multi-step dining concierge agent using OpenSearch over Yelp data with fallback query relaxation, and has enterprise data engineering experience at Capgemini migrating databases to Snowflake with robust ETL normalization and data-quality handling.”

Zhiwen Zhao

Screened

Junior Data Engineer specializing in cloud ETL and big data platforms

New York, NY3y exp

“Data engineer focused on transit/transportation datasets, building Spark-based pipelines that ingest from Oracle/APIs, apply PySpark data-quality fixes, and publish star-schema fact tables to Azure Data Lake. Experienced troubleshooting complex Spark failures (using checkpointing to manage long lineage) and operating Airflow-driven backfills and GitLab CI deployments for production DAGs.”

“Full-stack web developer with experience owning products from client discovery through launch and post-launch iteration, including a complete freelance build for an interior design firm and a large-scale React/TypeScript migration during an internship at Gateway Ticketing Systems. Stands out for balancing strong visual design with performance and SEO, and for improving emergency-use UX in an MVP product through flow simplification and A/B testing.”

“Analytics engineer/data analyst with Bank of America experience turning fragmented financial data across SQL Server, PostgreSQL, Kafka, and flat files into trusted Snowflake/dbt reporting models. Stands out for unifying disputed business definitions like churn and payment success rate, automating manual analysis in Python, and pairing strong data quality rigor with stakeholder adoption through self-service dashboards.”

“Built internal production LLM tools for engineering and support, including a customer-health assistant and a RAG-based incident explainer grounded in logs, metrics, and deploy data. Stands out for combining strong GenAI safety/evaluation practices with pragmatic backend engineering, delivering measurable impact like a 40% drop in data-help requests and answers in seconds instead of minutes or hours.”

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.”

“Backend/ML engineer who built and operated a production WhatsApp assistant end-to-end using a modern RAG stack, delivering >90% automation with sub-2-second latency. Shows strong depth in retrieval quality, observability, evaluation, and incident handling, and has also applied similar AI workflow patterns to a clinical diagnostic assistant processing medical PDFs.”

“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.”

“AI/ML engineer with production experience across both classical ML and Generative AI, including a real-time banking fraud detection platform at Deloitte and a RAG-based cybersecurity threat analysis feature at Accenture. Stands out for owning systems end-to-end—from feature pipelines and model tuning through deployment, monitoring, retraining, and API/platform reliability—with measurable impact on fraud accuracy, false positives, and SOC analyst efficiency.”

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.”

“AI engineer and current tech lead building a RAG-based multi-agent QA platform for financial document analysis at significant scale (40,000-50,000 documents). They combine Python, CrewAI, FastAPI, Hugging Face embeddings, Pinecone, and AWS SageMaker to deliver retrieval, calculation, summarization, forecasting, and visualization workflows, while leading a small cross-functional team.”

“Candidate has built multiple zero-to-one AI/full-stack products spanning bioinformatics search, rental marketplace semantic search, and an SDR agent for a hospitality startup. Particularly strong at turning LLM/embedding concepts into usable products with modular workflows, explainable outputs, and production-minded infrastructure.”

“Frontend-focused engineer with experience spanning healthcare, enterprise analytics, and real-time trading products. They have owned React/TypeScript dashboard surfaces end-to-end, including a hospital patient dashboard that cut latency by 50%, and have also shaped backend WebSocket contracts to make real-time systems scale.”

Harshal Sawant

Screened

Senior AI Engineer specializing in LLMs, RAG, and MLOps on multi-cloud

8y exp

“Built and productionized a secure internal RAG-based AI assistant (LangChain/FastAPI/FAISS on GCP), tackling real-world issues like latency, retrieval speed, and hallucinations—delivering 25% faster retrieval and 99.9% uptime. Also implemented scalable, reliable ML retraining orchestration with AWS Step Functions/SageMaker/Lambda and partners closely with compliance analysts to iteratively refine prompts and outputs to meet governance standards.”