Vetted PyTorch Professionals

“Backend engineer with experience modernizing a large-scale procurement platform at Jio Platforms by breaking a monolith serving 35 business units into Spring Boot microservices, improving uptime and cutting report latency ~30%. Also built high-concurrency FastAPI systems (200ms at ~500 concurrent users) with strong security (JWT/OAuth2/RBAC), event-driven Kafka integrations, and reliability patterns like exactly-once delivery for ~1M monthly triggers.”

“Backend/data engineer with hands-on production experience building FastAPI Python APIs and AWS-native platforms (Lambda/API Gateway, SQS, ECS Fargate) with Terraform + GitHub Actions CI/CD and strong reliability practices (JWT/RBAC, retries/timeouts, structured errors/logging). Also built AWS Glue ETL pipelines (S3/RDS to curated S3/Athena) with schema evolution and data quality controls, modernized legacy processing via parallel-run validation and phased cutovers, and has demonstrated SQL tuning impact (seconds to <200ms) plus incident ownership for batch pipeline SLAs.”

Sai Haritha Sardena

Screened ReferencesStrong rec.

Mid-level Full-Stack Software Engineer specializing in web, cloud, and AI/ML

Remote, USA7y exp

“Software engineer with experience at Wipro and Tylmen Tech owning customer-facing onboarding and real-time features end-to-end (React + Spring Boot) and building TypeScript/React apps backed by Node.js microservices (MongoDB, RabbitMQ). Strong in production reliability and fast iteration: feature-flagged rollouts, idempotent APIs/consumers, DLQs, and SLO-driven incident tooling, including an internal QA & release dashboard adopted by engineering and support teams.”

“AI-focused product engineer working on LLM routing, prompt engineering, and multimodal API integrations at production scale. They describe improving system accuracy, latency, and token usage, fine-tuning an internal model to reduce third-party API dependence, and adding safety guardrails through prompt-injection testing and red-team evaluation.”

“Robotics software engineer with deep ROS2 Humble/Nav2 experience who built an SDF-based navigation system (RRT* global planning + gradient-based local avoidance) and implemented scan-matching localization. Proven real-time performance debugging and optimization on hardware (Unitree B1), including halving compute-cycle latency and resolving ROS2 jitter/message-drop issues through explicit QoS and executor/callback-group design.”

“Built and deployed an Azure-based, fine-tuned CLIP visual retrieval system at Staples for a ~300k-item product catalog, improving edge-case recall by 12% by engineering a custom delta-similarity/dynamic-margin loss. Also has robotics experience using ROS2 for sensor/compute orchestration, including GPS-time-synchronized sensor triggering for robot swarms and latency-bounded optical-flow benchmarking for edge deployment.”

“Founder of pre-revenue cybersecurity startup Ceyepher Security; has already set up lead-intent sourcing, automated pipeline/CRM analytics, and outbound marketing. Plans to raise capital after landing first clients to demonstrate value via revenue, whitepapers, and customer testimonials; interested in studio support to accelerate sales. Mentions a disability that has enabled significant time honing computer science skills and is open to joining innovative work even outside their own company.”

“Built and productionized an LLM-driven document verification workflow for a construction firm’s submittals process, moving from a Vercel/Next.js prototype to a FastAPI + LangChain/LangGraph backend with background workers and multi-server deployment. Uses LLM tools (e.g., OpenAI Codex/Cloud Code) for rapid development and log-driven root cause analysis, and partners with customer teams on evaluation metrics and iterative improvements.”

“ML/AI engineer with hands-on experience shipping production computer vision and GenAI systems, including a fabric defect detection platform that combined vision models with agentic LLM workflows to reach 89% human-inspector agreement at 200 ms latency. Also built a RAG-based code QA tool for developers and emphasizes production monitoring, evaluation, caching, and reusable Python service design.”

“Built a production LLM-powered fraud detection platform at Wells Fargo, combining OpenAI/Hugging Face models with RAG-based explanations to make flagged transactions interpretable for risk and compliance teams. Delivered low-latency, real-time inference at high scale on AWS (SageMaker + EKS), with strong observability and security controls, reducing manual reviews and false positives in a regulated environment.”