.png){kind=logo}
Vetted FastAPI Professionals
Pre-screened and vetted.
Aleksandar Ilijevski
Intern Software Engineer specializing in AI systems and backend infrastructure
“Full-stack engineer with early-stage startup experience who shipped and owned production Next.js (App Router + TypeScript) features end-to-end, including auth-aware APIs, caching, and post-launch monitoring/iteration. Demonstrates strong performance and reliability chops across React UX optimization, Postgres analytics modeling/query tuning (validated via query plans), and durable ingestion workflows with retries/idempotency.”
Yukta Chikate
Mid-level Machine Learning Engineer specializing in safety-critical and uncertainty-aware ML systems
“Built and productionized an LLM-powered assistant for company documents and support questions, focused on reducing time spent searching PDFs/policies/tickets while preventing hallucinations by grounding answers in approved sources. Demonstrates strong production engineering (Kubernetes/orchestration, caching, monitoring, fallbacks) plus security-minded permissioning and close collaboration with operations/support stakeholders.”
Anna Niedzialek
Intern Software Engineer specializing in AI/ML and computer vision
“Backend-focused Python engineer who owned and deployed EcoHero, a recycling guidance app using FastAPI + Firebase with barcode lookup, ZIP-code-based state rules, and user history tracking backed by 50 state datasets. Has hands-on Kubernetes + Docker experience and uses GitHub Actions and GitOps-style PR workflows for consistent deployments, plus event-driven async processing patterns with idempotency and retries.”
Karnan Rajendran
Mid-Level Backend Engineer specializing in SaaS, FinTech, and AI document intelligence
“Full-stack engineer who built an AI-driven document analysis and processing workflow end-to-end, including large-document ingestion, queued async processing, and low-latency retrieval for user-facing flows. Demonstrated practical performance tuning (moving heavy work off request path, polling, caching) and Postgres optimization validated with EXPLAIN ANALYZE, plus durable workflow resilience via retries and dead-letter queues.”
Venkata Rampalli
Mid-level Backend/AI Software Developer specializing in data pipelines for FinTech and healthcare
“Data engineer/backend data services builder with end-to-end ownership of production pipelines for a Pfizer client, combining Python/SQL ingestion and transformation with strong data quality controls. Delivered measurable performance gains (~30% faster queries) and improved reliability through monitoring/alerting (Splunk, Prometheus/Grafana), structured logging, and incident response; also built internal REST APIs with versioning and caching and set up GitLab-based CI/CD with containerized deployments.”
Muaaz Syed
Mid-level AI/ML Engineer specializing in NLP and conversational AI
“ML/NLP engineer focused on real-time IT ops analytics, building a predictive maintenance/anomaly detection platform end-to-end (multi-source ETL, streaming, modeling, and production deployment on GCP/Vertex AI). Uses deep learning (LSTMs, autoencoders/VAEs) plus embeddings (SentenceBERT) and vector search to improve incident correlation and search, citing ~40% reduction in duplicate alert noise.”
Ramcharan Reddy
Mid-level Full-Stack Developer specializing in FinTech platforms and cloud-native microservices
“Backend engineer focused on AI-enabled systems, having built a production-style RAG pipeline (vector search + LLM) exposed via Python/Flask endpoints with strong observability and hallucination-reduction techniques. Demonstrates deep performance work in PostgreSQL/SQLAlchemy (5x faster analytics queries) and high-throughput optimization using Celery + Redis (800ms to 120ms latency, 3x throughput), plus schema-per-tenant multi-tenancy with tenant-aware middleware and logging.”
Rohan Varma Bandari
Mid-level Data Scientist & Generative AI Engineer specializing in LLMs and RAG
“Built production LLM + hybrid RAG and multi-agent orchestration systems at Wells Fargo to automate complaint document/audio transcript understanding and categorization, addressing vocabulary drift via embedding + vector index updates instead of frequent retraining. Strong in LLM workflow reliability (testing/benchmarks/observability) and stakeholder-facing delivery with explainability (citations/SHAP-style justifications) and Tableau dashboards.”
Sreeja Venkatakrishnan
Mid-level Full-Stack Developer specializing in Java/Spring and modern JavaScript frameworks
“Full-stack engineer with hands-on experience building real-time applications (Socket.io chat app) and data-heavy systems in banking/loan management. Comfortable across React and backend services (Spring Boot/Node), with a focus on scalable API design, database performance (indexing/pagination/caching), and deployment via CI/CD and cloud infrastructure.”
Harideep Balusa
Mid-level AI/ML Engineer specializing in FinTech risk, fraud detection, and GenAI/RAG systems
“Built and productionized Azure-based LLM/RAG systems for regulatory/compliance use cases, including automating analyst research and compliance report generation across large unstructured document sets. Demonstrates strong practical depth in hallucination mitigation, hybrid retrieval tuning (BM25 + embeddings), and production MLOps (Databricks, Cognitive Search, AKS, Airflow/MLflow), plus proven ability to deliver auditable, explainable solutions with non-technical compliance teams.”
Shivani Sharma
Mid-Level Software Engineer specializing in Cloud, DevOps, and MLOps
“Built and productionized a recommendation system from notebook prototype into a low-latency, scalable Cloud Run service using Docker, FastAPI, Terraform, CI/CD (GitHub Actions), and MLOps tooling (Vertex AI, MLflow). Experienced diagnosing real-time workflow issues using structured logging/ELK and GCP metrics, including resolving intermittent 504s by fixing unbounded SQL and adding caching. Also partners with sales/customer teams (Wasabi) to deliver tailored demos, troubleshoot, and drive onboarding/adoption.”
Nagaveda Sai Kumar Reddy Gajjala
Mid-Level Full-Stack Software Engineer specializing in distributed systems and cloud integrations
“Backend engineer with enterprise SaaS experience (Zoho) who owned an end-to-end cloud integration between Endpoint Central and ServiceDesk Plus, redesigning device onboarding across 64+ scenarios and building a fault-tolerant sync engine that recovered 100% failed transactions. Also built and operated production systems across the stack—FastAPI services with strong testing/observability, React+TypeScript portals, PostgreSQL performance tuning, and AWS deployments with real incident response (RDS CPU saturation resolved with zero downtime).”
Ojasmitha Pedirappagari
Mid-level AI Engineer specializing in LLMs, RAG, and agentic platforms
“Built and shipped a production RAG-based assistant that lets parents ask natural-language questions about their child’s learning progress, using pgvector retrieval (child-id filtered) and Redis caching to hit ~180ms latency. Implemented real-world guardrails and compliance (Llama Guard, COPPA, retrieval thresholds, fallbacks) with 99.5% uptime, and ran human-in-the-loop eval loops that improved satisfaction from 3.8 to 4.2 while serving 60k+ monthly users and reducing costs significantly.”
Ram Usarty
Mid-level Full-Stack Software Engineer specializing in cloud-native distributed systems
“Backend/platform-focused engineer who has shipped production LLM agents for messy research dataset submissions, turning manual validation into an automated, reliable ingestion pipeline. Strong on production hardening (streaming large uploads, strict schema/function-calling outputs, idempotency, RBAC) plus eval/monitoring loops that improved data quality, reduced support burden, and increased adoption.”
Gitesh Sagvekar
Mid-level Full-Stack Developer specializing in cloud-native healthcare applications
“Full-stack engineer with recent experience at Amgen building an internal healthcare data validation/transformation and workflow automation service: Python/FastAPI backend with REST APIs plus a React UI, designed around a canonical contract-first model to handle inconsistent upstream data. Operates production systems on AWS (EC2/ELB/S3/CloudFront) with strong focus on observability (structured logs, correlation IDs) and safe CI/CD-driven migrations; also has experience shipping quickly in ambiguous environments at TCS.”
keerthana s
Mid-level Backend Software Engineer specializing in Python/FastAPI on AWS
“Backend engineer with healthcare domain experience building AI-driven radiology workflow systems. Evolved tightly coupled APIs into secure, reliable FastAPI-based services by moving heavy imaging/data processing into idempotent asynchronous pipelines with retries, feature-flagged incremental rollout, and strong data-integrity controls (constraints, backfills, validation). Strong focus on defense-in-depth security for sensitive patient data (OAuth2/JWT, RBAC, and database-level protections).”
Zubair Shaik
Mid-level Full-Stack Developer specializing in AI-driven FinTech platforms
“Built and productionized an LLM-powered loan decisioning agent at Bank of America, integrating RAG with microservices to automate creditworthiness assessment and recommendations. Emphasizes real-world reliability and governance (EKS autoscaling, observability, SOC2/PCI security controls), and drove measurable outcomes including 20% faster loan decisions and a reduction in agent failures/fallbacks to under 2% through schema enforcement and confidence-based routing.”
Tara Worrell
Senior Software Engineer specializing in backend systems, AI/LLM integration, and cloud infrastructure
“Backend engineer with experience in highly regulated and high-stakes systems, including an airline crew messaging platform requiring near-zero-error real-time operations and a HIPAA-compliant mental health application built from an early-stage concept. They also show strong operational maturity, having owned a GoDaddy production incident through resolution and then led deployment pipeline improvements that reduced build failures by 40% and doubled deployment frequency.”
Alekya Battu
Mid-level Data Scientist specializing in machine learning, MLOps, and cloud analytics
“Senior data scientist with ~5 years’ experience building production ML/NLP systems in finance (Wells Fargo) and deep learning for sensor analytics in connected vehicles (Medtronic). Has delivered end-to-end platforms combining time-series forecasting with transformer-based NLP, including automated drift monitoring/retraining (MLflow + Airflow) and standardized Docker/CI/CD deployments; achieved a reported 22% precision improvement after domain fine-tuning.”
Hemanth Kumar Gajagiri
Mid-level Full-Stack AI Engineer specializing in agentic systems and scalable platforms
“AI-focused full-stack/DevOps engineer who goes beyond using copilots and has built production-oriented LLM systems such as natural-language-to-SQL and structured insight extraction pipelines. Stands out for treating AI as an accelerator rather than a replacement, with a strong emphasis on guardrails, validation, observability, and safe deployment practices in agent-based and distributed systems.”
Tarun Majhi
Mid-level AI Software Engineer specializing in FinTech and LLM systems
“Engineer with hands-on experience designing and leading multi-agent AI development workflows, including a LangGraph-based system that automated parts of a RAG pipeline and significantly reduced development time. Stands out for treating AI agents like an engineering team, with clear architecture, handoff schemas, validation, and supervisor-driven conflict resolution.”
Parnashri Nandam
Mid-level AI Engineer specializing in LLMs, RAG, and production ML systems
“Backend engineer who built an AI-powered grant matchmaking platform for researchers and professors, combining semantic matching, embeddings, and Semantic Scholar enrichment with rule-based eligibility filters. Stands out for pragmatic AI engineering: they focused on reliability through confidence scoring, logging, manual validation, and production-minded backend design.”
Hard Parikh
Mid-level Software Engineer specializing in data platforms, distributed systems, and applied AI
“AI/full-stack product engineer currently owning Fleck Intelligent Survey Chatbot at E15, a production RAG analytics assistant embedded in Compass Group dashboards for 300+ field operators. Stands out for combining LLM orchestration, analytics engineering, and strong systems thinking—cutting hallucinated numeric answers from 14% to 2%, reducing backlog 62%, and previously delivering a low-level protocol redesign at Amadeus that cut P99 latency by 56%.”
Manoj Manjunatha
Mid-level Software Engineer specializing in full-stack systems and applied AI
“Built the backend for “codeGuard,” an AI-powered static code analysis platform, using FastAPI and Docker. Structured the system into API/service/execution layers and addressed heavy-workload container resource/cleanup issues via strict CPU/memory limits and a queued execution model.”