Vetted FastAPI Professionals

Anay Dongre

Screened

Junior Machine Learning Engineer specializing in GenAI and LLM fine-tuning

Pomona, California1y exp

“Robotics software engineer focused on hard real-time autonomy for legged robots, building a quadruped navigation stack that combines vision SLAM with MPC and maintains a deterministic 500Hz control loop. Deep performance optimization experience across CUDA (sub-2ms perception latency), ROS 2/DDS real-time tuning, and motion planning (cut 500ms spikes to sub-5ms). Also designed distributed ROS 2 + Zenoh communications between quadrupeds and aerial drones and validated robustness under lossy wireless conditions.”

Jarin Tasnim

Screened

Staff/Lead Software Engineer specializing in distributed data and ML platforms

Mountain View, CA6y exp

“Defense-domain AI engineer who built a production ReAct-style RAG system for military training data/material generation, scaling to ~1000 users and cutting generation time by 50%. Also has experience designing GPU-cluster parallel computation with PyTorch and handling production incidents involving database performance and schema design.”

Sachin Dulla

Screened

Mid-level AI/ML Engineer specializing in NLP, fraud detection, and MLOps

Kentwood, MI3y exp

“Built and deployed a domain-specific LLM chatbot for research/support, cutting manual effort by ~50%. Demonstrates strong applied LLM engineering: RAG, prompt grounding with citations and fallbacks, embedding/top-k tuning, and production monitoring (confidence, latency, feedback loops). Experienced orchestrating agent workflows with LangChain-style pipelines and continuous evaluation to maintain reliability.”

Bhavishyasai Chigurupati

Screened

Mid-Level Data/ML Engineer specializing in Generative AI and cloud data platforms

Overland Park, KS5y exp

“Built and productionized an LLM-based financial document analysis system using a RAG pipeline, including robust ingestion/chunking/embedding workflows, vector DB retrieval, and an AWS-deployed FastAPI service containerized with Docker. Demonstrates strong applied expertise in improving retrieval quality and latency at scale, plus hands-on experience debugging agentic/LLM workflows with monitoring and trace-based analysis while supporting demos and customer-facing adoption.”

Umair Khokhar

Screened

Director-level Engineering Leader specializing in AI Platforms for Enterprise B2B SaaS

San Francisco, CA15y exp

“Technical leader/player-coach who architected and shipped an end-to-end computer vision pricing system for a major North American auto seller, using Go + Ray + AWS SageMaker in a low-latency distributed inference architecture. Strong in production governance (logs/tracing/guardrails/AppSec), reliability incident ownership (DNS limits affecting 20% traffic), and measurable delivery acceleration (deployment cycle 16→4 days; delivery speed 5→2 days) through process optimization and AI-assisted enablement.”

Ido Avnon

Screened

Junior Software Engineer specializing in AI, geospatial, and full-stack applications

Seattle, WA5y exp

“Early-stage startup engineer with experience at pre-seed and roughly Series A-stage companies, including Rage Events Inc. Shipped production full-stack features with Next.js/TypeScript, designed Postgres-backed workflow systems, and took ownership of post-launch support and fixes in fast-moving environments.”

“Built practical automation systems spanning an NLP-based news classification pipeline and a WhatsApp interaction agent. Shows strong instincts around production reliability—using structured outputs, schema validation, idempotency, retries, and clarification flows to prevent bad actions in real-world messaging workflows.”

“AI/LLM engineer who has shipped production RAG copilots and multi-agent workflows, including a real-time Llama3 (Ollama) copilot backend handling 12k+ concurrent queries at 99.9% uptime. Deep on orchestration (Langflow/Airflow/Kubernetes), reliability evaluation (hallucination detection, p95 latency, token cost), and monitoring (Prometheus/Grafana), with demonstrated stakeholder-facing analytics delivery via Tableau.”

“Entry-level candidate who built NYC Lens, a real-time Gemini-based multi-agent system that processes live camera input, identifies landmarks, and returns structured contextual insights. Despite being a fresher, they show hands-on experience with deployment on Cloud Run, modular orchestration, noisy-data handling, and reliability patterns like retries, fallbacks, and explicit state management.”

“Built and owned production GenAI systems for both infrastructure automation and customer support. Most notably, they created a self-healing multi-cloud incident response system that automated 65% of tier-1 alerts and reduced application crashes by 75%, and also shipped a hybrid RAG support triage agent that automated 60% of tier-1 inquiries with human escalation guardrails.”

SaiSindhu Beeravolu

Screened

Mid AI/ML Engineer specializing in LLMs, RAG, and healthcare AI

Illinois, USA4y exp

“Healthcare ML/AI engineer with production experience at UnitedHealth Group, including an end-to-end readmission prediction system built on 50M+ patient records that improved accuracy by 18% and reduced preventable readmissions by 12%. Also shipped a clinically grounded LLM/RAG referral generator with human-in-the-loop safety controls, showing strong depth in regulated, high-stakes AI systems.”

Almas Fathimah

Screened

Senior AI Engineer specializing in Generative AI and ML platforms

USA7y exp

“Built and owned a production RAG-based conversational AI system at Entera for real estate analysis, taking it from experimentation through AWS deployment, monitoring, and iterative improvement. Demonstrates strong practical judgment in retrieval design, LLM safety, and scalable Python service architecture, with measurable impact including 30-40% reduction in manual analysis time and roughly 30% better response accuracy.”

“Backend/infrastructure-focused engineer building AI-agent products for small businesses, including a customer-service agent platform with intent routing, RAG over Pinecone, and external booking API integration. Has shipped Python/FastAPI services with JWT auth, versioned APIs, Docker deployments to AWS EC2 via GitHub Actions, and production monitoring with Prometheus/Grafana.”

“Full-stack engineer with hands-on experience building and shipping production web products across AI, frontend, backend, and DevOps. Notably built an end-to-end resume-job matching platform during an internship that processed 1000+ resumes/day and cut recruiter screening effort by 60%, and later shipped an internal operations dashboard at CHS with measurable performance gains.”

Shreyas Kumar

Screened

Entry-level AI Engineer specializing in LLMs and applied NLP systems

Syracuse, NY1y exp

“Built Lumo, a real-time voice AI companion, owning the product end-to-end across React/TypeScript, FastAPI WebSockets, and PostgreSQL. Stands out for combining deep full-stack systems thinking with voice UX polish, reliability instrumentation, and configurable parent-control guardrails in a multi-tenant setup.”

SUMANTH Metimath

Screened

Mid-level Data Scientist specializing in ML, LLMs, and AI systems

Potsdam, NY3y exp

“Candidate takes a pragmatic approach to AI-driven development, using AI as a productivity and learning aid while emphasizing personal understanding and code validation. They have hands-on experience applying AI-assisted workflows to a resume analysis and ATS scoring project, including code generation, debugging, parsing logic improvement, and testing.”

Raj Shrinivasan

Screened

Principal AI Engineer specializing in agentic systems and cloud-native platforms

Cleveland, OH23y exp

“Built a production RAG-powered analytics copilot at Aya Healthcare for operations leaders and analysts on a large healthcare staffing platform processing over a billion telemetry records annually. Stands out for strong production-minded agent engineering: deterministic orchestration, grounding-first design, deep observability, and data-driven workflow changes such as confidence-based human review for a PR review agent.”

“Built an AI-driven trading mentor/analytics system combining a React frontend, Spring Boot backend, and FastAPI ML service for stock risk and indicator analysis. Stands out for a pragmatic approach to AI-assisted development: uses AI for acceleration, but manually reviews code line by line, validates outputs against real market behavior, and adds safeguards for unreliable financial data.”

Bhanu Akepogu

Screened

Mid Software Engineer specializing in backend, full-stack, and AI systems

USA3y exp

“Full-stack engineer with 3+ years of backend and frontend experience who has built production AI products for enterprise document and policy workflows. Stands out for owning end-to-end systems that combine React, FastAPI, RAG, vector search, and AWS deployment, with measurable impact including 65% less manual review time and significantly faster knowledge-query resolution.”

“Solo builder of multiple AI products including CareerCopilot and ResearchMind AI, with hands-on experience across full-stack engineering, streaming LLM UX, grounded research workflows, and conversational AI. Also brings research depth as first author of an IEEE FG2026 paper on out-of-distribution deepfake detection, plus early-stage R&D experience building real-time robotics control software during an internship at ISRO's Space Applications Centre.”

“Systems-oriented engineer with strong Python depth who repeatedly improves throughput in complex, ambiguous environments by designing pragmatic human-in-the-loop automation. Notable wins include a record-validation workflow that put a department over six months ahead of schedule and saved $500K+, plus AI-assisted and migration tooling integrated with Jama for engineering requirements and traceability.”

“Full-stack product engineer with hands-on ownership of a B2B e-commerce catalog at Mphasis, spanning React/TypeScript, Java/Spring Boot, and Postgres, with strong evidence of performance tuning and UX improvement. Stands out for tracing production issues across layers, leading an Angular-to-React migration, and pairing product instinct with rapid AI prototyping—placing 2nd in an advanced AI hackathon.”