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Vetted FastAPI Professionals
Pre-screened and vetted.
Mid-level Generative AI Engineer specializing in LLMs, RAG, and NLP systems
Junior Software Engineer specializing in ML inference infrastructure
Junior Full-Stack Software Engineer specializing in backend, cloud, and AI systems
Mid-level Java Software Engineer specializing in backend systems and AI-integrated platforms
Junior Full-Stack Software Engineer specializing in cloud, distributed systems, and AI
Mid-level ML Engineer specializing in AI systems and LLM infrastructure
Senior Backend Engineer specializing in cloud-native microservices and AI integrations
Senior Full-Stack Engineer specializing in Python, cloud-native SaaS, and data pipelines
Senior Full-Stack Software Engineer specializing in AI/LLM-powered web applications
Senior Full-Stack Python Engineer specializing in AI/LLM-powered web applications
Senior Python Full-Stack Engineer specializing in cloud-native scalable systems
Senior Backend/Full-Stack Engineer specializing in cloud-native APIs and data platforms
Senior Machine Learning Engineer specializing in Generative AI and MLOps
Senior Full-Stack Engineer specializing in Python back-end systems and scalable web apps
Senior Full-Stack Engineer specializing in Python, React, and cloud-native AI features
Mid-level AI Engineer specializing in agentic LLM workflows and RAG systems
Affan Ahmed
Senior Full-Stack & Machine Learning Engineer specializing in scalable SaaS and cloud AI
“Frontend engineer who led an enterprise self-serve analytics dashboard end-to-end using a micro-frontend React/TypeScript architecture with strong integration discipline (contracts, CI gates, ADRs). Demonstrated measurable performance wins (35% faster LCP) through code splitting, lazy loading, and tighter Redux subscriptions, and uses feature flags plus automated E2E coverage for controlled rollouts.”
Rukmini Pisipati
Junior AI/ML Engineer specializing in LLM automation and NLP
“Built and shipped a production LLM hallucination detection and monitoring pipeline using semantic-level entropy (embedding-clustered multi-generation variance) to flag unreliable outputs in downstream automation. Implemented a scalable async architecture (FastAPI + Docker + Redis/Celery) with strong observability (structured logs + PostgreSQL) and developed evaluation loops combining controlled prompts and human review; also partnered with non-technical stakeholders on AI-driven form validation/document processing.”
Vishesh Patel
Junior AI/ML Engineer specializing in Python ML, NLP, and model deployment
“Built and productionized a real-time social-media sentiment analysis system used by a marketing team to monitor brand/campaign performance. Experienced in orchestrating LLM workflows with LangChain (validation → prompting → parsing → post-processing), plus monitoring, retraining, and RAG-style retrieval using embeddings/vector stores to keep outputs reliable over time.”