Vetted FastAPI Professionals

Likitha Kukunarapu

Screened References

Mid-level Applied AI Engineer specializing in data engineering and healthcare AI

Remote, USA3y exp

“Built production LLM agents spanning document Q&A, financial insight generation, and ERP-like operational data workflows, with a strong focus on reliability, grounding, and evaluation. Stands out for translating LLM systems into measurable business outcomes, including 70%–80% support workload reduction and a fallback-rate improvement from 18% to 8% through targeted RAG iteration.”

“Frontend/full-stack engineer with enterprise experience at Infosys and hands-on ownership of a full-stack educational platform, MicroExplore. Built React UI architecture plus FastAPI/MongoDB CRUD features end-to-end, and stands out for candid technical judgment around tradeoffs, maintainability, and security gaps he would address in a production-grade next iteration.”

“Built and productionized an internal GenAI chatbot that makes company policy/SOP knowledge instantly searchable, using a secure RAG architecture on AWS (Bedrock/Titan embeddings/OpenSearch Serverless, Textract/Lambda/S3 ingestion, Claude 3 Sonnet). Demonstrates strong MLOps/orchestration experience (Airflow, Step Functions with Lambda/Glue/SageMaker) and a rigorous reliability approach (RAGAS metrics, A/B testing, citation validation, monitoring), including collaboration with compliance stakeholders via review dashboards.”

“AI Software Engineer (student employee) at University of Washington IT who helped deploy "Purple," a governed, explainable LLM platform on Azure used by 100,000+ students/faculty/staff. Independently led scalable reliability efforts by building automated agent quality/load/red-team testing and CI/CD health validation (Playwright/Node.js, Azure DevOps), and previously built an explainable AI scheduling assistant for clinical operations at Proliance Surgeons.”

“Backend/platform engineer who has owned a real-time data ingestion/processing/reporting API built with FastAPI, Redis, and Celery, including performance tuning via query/index optimization, caching, and async workers. Strong Kubernetes + CI/CD + GitOps (ArgoCD) experience, plus hands-on monitoring/logging (Prometheus/Grafana/ELK) and a Kafka/Spark real-time streaming project from their master’s program.”

“Built and shipped a production RAG system (“TROJAN KNOWLEDGE”) for answering questions over technical PDFs, using a 3-stage retrieval stack (BM25 + FAISS + cross-encoder) to lift F1 from 71% to 84%. Drove major performance gains with a 3-level cache (memory/Redis/disk) cutting latency from ~200ms to ~10ms, and added Prometheus/Grafana monitoring plus LangChain-based fallback logic to handle OpenAI rate limits under load.”

“Engineering leader with recent hands-on depth across TypeScript/React, Go, and Python who led a 15-person team through a zero-downtime migration from a legacy monolith to a Module Federation architecture. Has B2B SaaS experience in an insurance agent portal, combining security and performance work through RBAC, OAuth 2.0, and edge-based authentication.”

Preethi Bandari

Screened ReferencesStrong rec.

Mid-level Full-Stack Developer specializing in scalable web applications

Memphis, TN5y exp

“Developer who uses AI tools pragmatically to accelerate coding while keeping full ownership of system design and decision-making. Emphasizes rigorous review, testing, and alignment with architecture, security, and performance standards, and stays current on AI through both industry sources and hands-on experimentation.”

“Backend-leaning full-stack engineer who has built AI-powered analytics products from 0→1, including a predictive analytics dashboard and an AI orchestrator for natural-language-to-database querying. Particularly strong in making LLM systems production-safe through schema validation, self-healing retries, monitoring, and retrieval optimization, with quantified impact on cost, latency, and quality.”

Rathi Anand

Screened ReferencesStrong rec.

Senior Full-Stack Software Engineer specializing in Insurance, FinTech, and AI/ML applications

Dublin, CA17y exp

“AI/backend engineer who fine-tuned and deployed a production LLM chatbot using a LangChain + FAISS RAG pipeline, improving latency with PEFT/LoRA and driving strong business impact (40% customer adoption; 92% satisfaction). Also served as technical lead on a data aggregation system for underwriting/quoting, introducing GraphQL for more efficient, maintainable querying and applying CDC to keep cached ranking data fresh at scale.”

“ML/AI engineer with hands-on ownership of production recommendation and GenAI systems, spanning experimentation, deployment, monitoring, and iteration. Stands out for delivering measurable outcomes—22% CTR lift, 15% conversion lift, and a 30% reduction in support tickets—while demonstrating strong judgment on latency, cost, and safety tradeoffs in real-world systems.”

“Software engineer with 2 years of professional full-stack experience plus a CS master's journey in the US, who has since focused heavily on building hackathon-winning AI systems. Stands out for combining production-minded backend architecture, TypeScript-heavy reliability work, and multi-agent LLM applications spanning physical security and insurance claims automation.”

“Product engineer/designer with founder mindset who shipped a blockchain-enabled investor group/governance platform using Next.js (App Router), TypeScript, Prisma/Postgres, and Temporal. Emphasizes auth-centric onboarding (SSO + embedded wallet) to make dApp UX feel more like SaaS, and brings strong reliability practices (idempotent retries, reconciliation) plus experience demoing to investors and operating in seed-stage teams (ex-Vouched).”

Preetham Dandu

Screened ReferencesModerate rec.

Junior Software Engineer specializing in backend, distributed systems, and cloud platforms

Stony Brook, NY1y exp

“MS candidate with strong backend/data engineering focus who builds research and data systems with production-grade rigor (reproducibility, observability, restartability). Has hands-on experience securing and scaling FastAPI-based gateways in front of Java microservices, leading SQL Server→Snowflake migrations with dual-write/feature-flag rollouts, and hardening Kafka-based fleet-tracking systems against out-of-order and duplicate events.”

“Backend/platform engineer who owned an end-to-end financial data ingestion and validation system (Python/Django/FastAPI, Postgres, AWS), including large-file performance tuning, auditability, and CI/CD. Strong Kubernetes/EKS + ArgoCD GitOps practitioner and has delivered both Kafka-based real-time transaction streaming and a legacy on-prem stack migration to AWS (ECS Fargate, RDS, S3, Secrets Manager) with controlled cutovers and data consistency validation.”

Laxminarayana Yaga

Screened

Mid-level AI/ML Engineer specializing in Generative AI, RAG, and MLOps

Missouri, USA4y exp

“Built and deployed a production RAG pipeline at PNC Financial Services to let risk/compliance analysts query millions of internal financial documents in natural language, reducing manual search and speeding regulatory validation. Demonstrates deep practical experience with large-scale document ingestion/OCR cleanup, retrieval performance tuning (hierarchical indexing, caching), and LLM reliability controls (grounding, citations, abstention), plus cloud orchestration on Azure and AWS.”

“AI/LLM engineer with healthcare domain experience who built a production clinical support “chart bot” for Molina, including PHI-safe ingestion of 200k+ PDF policies, vector retrieval, and a fine-tuned LLaMA served via vLLM on ECS Fargate. Demonstrated measurable performance wins (HNSW + namespace partitioning; 30% inference latency reduction) and a rigorous evaluation/monitoring approach, while partnering closely with nurses and operations teams to shape workflows and guardrails.”

“Built a production AI-powered sales automation system that reads inbound product enquiry emails, extracts structured data, and routes decisions via a rules-based workflow integrated with a product database. Leverages Gemini structured outputs/schema plus option-based prompting and validation to keep responses reliable, and optimizes latency by breaking agent reasoning into smaller LLM calls; evaluates workflows with LangSmith and metrics like completion rate and accuracy.”

Tarang Nair

Screened

Mid-Level Full-Stack Software Engineer specializing in cloud-native APIs and AI apps

Oakland, CA5y exp

“Early-stage startup full-stack engineer who built an inventory and billing management product for retail shop owners in India, iterating quickly based on customer feedback. Has hands-on experience designing scalable cloud infrastructure on GCP with Kubernetes and applying clean architecture for testable, modular systems; also built internal monitoring dashboards for error and performance visibility.”

“Backend Python engineer focused on production-grade automation and reliability, with hands-on experience designing scalable API systems on PostgreSQL and making pragmatic architecture calls (modular monolith over premature microservices). Demonstrated measurable performance wins (50–60% latency reduction) and strong operational rigor via observability, incremental rollouts/feature flags, and security patterns like JWT + RBAC + database row-level security.”

“LLM/agent production specialist with strong customer-facing and pre-sales chops: turns demo-grade prototypes into reliable, compliant deployments using RAG tuning, guardrails, evals in CI, and observability with staged rollouts/rollback. Known for engineering-first workshops (including live break-and-fix on retrieval misses, tool timeouts, and prompt injection) that win over skeptical senior developers and drive adoption.”

Siva Pothuru

Screened

Mid-level AI/ML Engineer specializing in LLMs, MLOps, and cloud-native ML

San Antonio, TX5y exp

“LLM/agent engineer at USAA who built a production GPT-4o RAG conversational assistant for financial analysts, focused on regulatory interpretation and internal documentation search. Emphasizes compliance-grade reliability with strict grounding, safe fallbacks, and full auditability via MLflow/DVC plus human-in-the-loop review; reports ~45% reduction in ticket resolution time.”