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Vetted FastAPI Professionals
Pre-screened and vetted.
Medhovarsh Bayyapureddi
Intern Machine Learning & Full-Stack Engineer specializing in computer vision and healthcare AI
“AI/ML-focused backend engineer who shipped two production systems: PersonaPal (agentic LLM chatbot with RAG, FAISS-based retrieval, and Redis semantic caching) and CervixScan (clinical diagnostics platform with PostgreSQL data modeling and human-in-the-loop safety for low-confidence predictions). Demonstrates strong performance/reliability work (indexed vector search, caching, query optimization to ~200ms) and end-to-end ownership from orchestration design through deployment.”
Humani Korem
Mid-level Software Engineer specializing in data pipelines and backend APIs
“Data engineer with Webster Bank experience owning end-to-end pipelines (APIs + databases) processing millions of records/day, improving data quality (25–30% fewer issues) and reliability (~99.9% successful runs). Built resilient external data ingestion/scraping systems (schema-change validation, idempotent backfills, monitoring/alerts) and shipped a FastAPI service exposing curated datasets with versioning and consistently low latency.”
Hiya Kothari
Intern Full-Stack Software Engineer specializing in AI/ML and cloud
“Built a Python-based geospatial machine learning backend for PFAS contamination risk mapping, including reproducible feature pipelines, ensemble modeling, and a FastAPI layer for visualization/analysis. Emphasizes data integrity and robustness (CRS/coverage checks, fail-fast validation) and has led safe backend refactors using feature flags, idempotent backfills, and Postgres RLS for secure, queryable results delivery.”
Yashodhar Pansuria
Senior Full-Stack Java Developer specializing in capital markets and trading systems
“Backend/data engineer with production experience in payment initiation/processing services built in Python/FastAPI, emphasizing reliability patterns (JWT/RBAC, timeouts, retries, circuit breakers). Has delivered AWS deployments on ECS (ALB, autoscaling, CI/CD to ECR) plus Lambda-based reporting, and built AWS Glue ETL pipelines with schema evolution and CloudWatch monitoring. Also modernized a legacy SAS reporting platform to Python/PostgreSQL with regression parity testing and parallel-run migration, and achieved a 70% SQL performance improvement.”
Somil Shah
Mid-level AI/ML Engineer specializing in generative AI, RAG platforms, and LLM agents
“AI/LLM engineer who has shipped 10+ production applications, including InvestIQ on GCP—a production-grade RAG due-diligence engine that ethically scrapes web/PDF sources, builds a ChromaDB knowledge base, and delivers analyst-style dashboards plus a citation-backed chat copilot. Deep focus on reliability (evidence-only answers, hard citations, refusal gating), retrieval tuning, and orchestration (Airflow/Cloud Composer), plus multi-agent systems (CrewAI with 7 specialized finance agents).”
Vamshi Korra
Mid-Level Full-Stack Software Engineer specializing in FinTech and cloud-native microservices
“Open-source React dashboard/visualization library maintainer focused on runtime performance and API clarity. Led a significant effort to eliminate severe render lag on large live-updating datasets through profiling-driven refactors (normalized state, memoized selectors) and locked improvements in with CI, linting, and documentation that reduced regressions and improved external contributor onboarding.”
Hritvik Gupta
Mid-level AI Engineer specializing in LLMs, RAG, and healthcare AI
“Built and scaled an AI-powered voice/chat patient engagement platform at Penn Medicine from early prototype into production clinical workflows, focusing on latency, edge cases, and user trust. Strong in LLM reliability engineering (structured prompts, validation/fallbacks), real-time troubleshooting with observability, and cross-functional enablement through pilots, demos, and sales/customer partnership.”
Archana yaramala
Mid-level AI/ML Engineer specializing in deep learning, MLOps, and LLM applications
“Built and deployed production LLM assistants for internal Q&A and customer-feedback summarization, emphasizing reliability (RAG, prompt tuning, validation/whitelisting) and privacy safeguards. Improved adoption by adding explainable outputs and a user feedback mechanism, and has hands-on orchestration experience with Aflow and Azure Logic Apps.”
Bala Venkateswarlu K
Mid-level Data Scientist specializing in Generative AI, NLP, and MLOps
“Built and deployed an LLM-powered claims-document summarization system (insurance domain) that cut agent review time from 4–5 minutes to under 2 minutes and saved 1,200+ hours per quarter. Hands-on across orchestration and production infrastructure (Airflow retraining DAGs, Kubernetes, SageMaker endpoints, FastAPI) and recent RAG workflows using n8n + Pinecone, with a strong focus on reliability, cost, and explainability for non-technical stakeholders.”
“Backend-focused intern who built and refactored the backend for an LLM-driven gifting mobile app using FastAPI, tackling high-latency LLM + product-API workflows. Implemented async worker-pool/queue processing with Redis caching plus retries/fallbacks, cutting end-to-end suggestion latency from ~4–5 seconds to ~1 second while improving reliability and rollout safety via staged migrations and testing.”
Daniel Berhane Araya
Senior AI/ML Engineer specializing in production-grade LLM systems for regulated finance
“AI/LLM engineer with published work who built FinVet, a production financial misinformation detection system using multi-pipeline RAG, confidence-based voting, and evidence-backed outputs (F1 0.85, +37% vs baseline). Also built NexusForest-MCP, a Dockerized Model Context Protocol server exposing structured global deforestation/carbon data via SQL tools for reliable LLM tool use. Previously delivered borrower risk-rating (PD) models at BMO Financial Group that were validated and integrated into an enterprise credit system through close collaboration with credit officers and portfolio managers.”
maheen Adeeb
Senior Machine Learning Engineer specializing in LLMs, speech AI, and RAG systems
“AI engineer with production experience building multilingual speech-to-speech translation pipelines (ASR + LLM) for enterprise/media, focused on reliability at scale. Has hands-on orchestration experience (including IBM Watson contexts) and emphasizes production evaluation/monitoring using a mix of traditional metrics and LLM-based evaluators to catch quality regressions while balancing latency and cost.”
Ioannis Gregos-Mourginakis
Mid-Level Software Engineer specializing in blockchain, data pipelines, and functional programming
“Built an LLM-powered production agent that scraped X.com data to categorize and profile accounts, deployed in Docker with automatic relaunch for high uptime. Interview details were limited, but the candidate does have hands-on experience shipping an AI automation workflow.”
“Analytics-focused candidate with internship and project experience at Recotap and CoUnderscore, combining SQL, Python, and BI dashboards to turn messy marketing and engagement data into decision-ready reporting. Stands out for tying analytics work to business outcomes, including ~15% CTR improvement, identifying ~40% misattributed spend, and enabling a ~$75K budget shift through better targeting.”
Chethan Thimapuram
Mid-level AI Engineer specializing in LLMs, MLOps, and healthcare NLP
“Built a production, real-time clinical documentation system at HCA that converts doctor–patient conversations into structured clinical summaries using speech-to-text, LLM summarization, and RAG. Demonstrated measurable gains from medical-domain fine-tuning (clinical concept recall +18%, ROUGE-L 0.62 to 0.74) while meeting HIPAA constraints via PHI anonymization and encryption, and deployed via Docker/FastAPI with CI/CD and monitoring.”
SathwikReddy Nethani
Mid-level AI/ML Engineer specializing in GenAI, NLP, and financial systems
“GenAI/ML engineer with hands-on experience building production financial intelligence and document summarization systems at Citibank. Stands out for combining LLM fine-tuning, hybrid RAG, multi-agent workflows, and strong MLOps/observability practices to deliver measurable business impact, including 60% faster analyst retrieval, 31% higher precision, and 99%+ uptime.”
Vimala Devi
Mid-level AI & Machine Learning Engineer specializing in FinTech
“ML/AI engineer with hands-on experience building production systems in financial services, including a real-time underwriting analytics platform at Hartford Financial Services. Stands out for combining classic ML, low-latency API deployment, monitoring, and emerging LLM/RAG design patterns, with measurable impact including 20% better decision accuracy, sub-200ms latency, and 5M+ records processed daily.”
kartikeya tiwari
Senior Software Engineer specializing in AI systems and platform engineering
“Backend/AI engineer with experience owning production systems in fintech and product startups, including a predictive scaling platform that cut AWS spend by 40% and an ambiguous social-intelligence feature that doubled MRR from $50K to $100K. Also building AI search and document-processing workflows, with reported 99.7% extraction accuracy and hands-on use of both classical forecasting and modern LLM stacks.”
“Software engineer currently building AI-powered backend systems for interview analysis, with end-to-end ownership of an LLM-based monitoring platform. Stands out for combining practical product delivery in an ambiguous early-stage environment with measurable impact: over 40% reduction in manual review effort and roughly 20% lower inference cost.”
Utkarsh Patel
Mid-level Full-Stack Engineer specializing in AI products and LLM systems
“AI-native software developer who has built a highly structured workflow around Claude, Cursor, design agents, and SpecKit to plan, design, implement, and test features end to end. They also use multi-agent setups with sub-agents and git worktrees, and have experience acting as a tech lead for AI agents by assigning roles, guiding execution, and reviewing outputs.”
Koushik Ravikumar
Mid-level Full-Stack Engineer specializing in cloud-native Java and AI platforms
“Full-stack engineer with strong cloud and platform experience spanning React, Go, AWS, Kafka, and Terraform. Has led complex migrations from monolithic/containerized systems to microservices and cloud deployments, built compliance-oriented logging infrastructure, and improved a broken frontend codebase to achieve a 3x performance gain while making it easier for other developers to extend.”
Venkateswarlu Mopidevi
Mid-level Full-Stack Engineer specializing in AI and cloud platforms
“Built end-to-end product features spanning full-stack web development and LLM-powered systems in an early-stage startup environment. Notably shipped an AI financial assistant chatbot with agent routing, validation, fallback handling, and production monitoring, and also owned a scheduling system integrating Next.js, backend APIs, database design, and Google Calendar OAuth.”
Nitesha Paatil
Mid-level Software Engineer specializing in backend web applications and APIs
“Backend-leaning full-stack engineer who has shipped both a SaaS analytics/A-B testing platform and an AI-driven fraud monitoring product in production. Stands out for combining React/TypeScript frontend work with Python/Java backend systems, event-driven architecture, and practical LLM integration grounded by validation and human analyst feedback, with measurable impact on engagement, performance, fraud accuracy, and false positives.”
Yujun Liu
Junior AI software engineer specializing in agentic workflows and cloud platforms
“AI/full-stack builder who has owned end-to-end LLM features in both creative tooling and education workflows. Built a multi-agent film-production workspace for Backlot and an automated ingestion pipeline at iOffer.AI that cut counselor manual entry by 70%, with hands-on depth in orchestration, validation, monitoring, and human-in-the-loop system design.”