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Vetted FastAPI Professionals
Pre-screened and vetted.
Pavan Kumar Malasani
Mid-level AI/ML Engineer specializing in financial risk, fraud detection, and GenAI
“GenAI/ML engineer in Citigroup’s finance environment who has deployed production RAG systems for investment banking under strict privacy and model-risk constraints. Built an internal-VPC Llama2 + Pinecone + LangChain solution with NER redaction and citation-based verification to prevent hallucinations, delivering major time savings, and also partnered with global finance executives to ship an AI early-warning indicator for treasury/liquidity risk.”
Yukti Kamthan
Senior Software Engineer specializing in AI/ML and data systems
“Built and shipped production LLM/AI agent systems including an NL-to-SQL query agent with semantic search and Redis-based caching, using schema-aware prompting and threshold validation to reduce hallucinations. Has orchestration experience running ML microservices on Kubernetes and automating event-driven insurance (P&C) workflows (claims/policy + fraud checks), reporting ~60% manual overhead reduction and ~99% uptime, with strong monitoring/drift-detection and business-facing Power BI reporting.”
HEMANTH KUMAR KOTTAPALLI
Mid-level Machine Learning Engineer specializing in GPU-accelerated LLMs and MLOps
“Built and deployed a production LLM-powered decision-support system for supply-chain planners that explains demand forecast changes using grounded retrieval from sales, promotion, inventory, and supplier data. Implemented strict anti-hallucination guardrails and latency optimizations, deployed as a real-time AWS API with monitoring, and reported ~15% forecast accuracy improvement and ~12% supply-chain risk reduction. Experienced orchestrating data/ML/LLM workflows with Airflow, LangChain/LangGraph-style patterns, and AWS Step Functions while partnering closely with non-technical business users via demos and example-based requirements.”
Anishkumar Mahalingam Iyer
Intern Software Engineer specializing in AI/ML infrastructure and applied machine learning
“Interned at Rivian where they built and deployed a production Whisper-based ASR + LLM real-time event labeling pipeline to help autonomous-vehicle engineers diagnose failures and route issues to triage teams. Also built a stateful multi-agent "Code Partner" developer assistant using LangGraph/LangChain (planner/router/coder/critique/tester) with evaluation, adversarial testing, and stakeholder-friendly communication practices.”
Mason Acevedo
Mid-Level Software Engineer specializing in data pipelines, APIs, and ML
“Software engineer whose recent work includes co-designing and building a "Shared Profile" feature for a social event-planning app (Again, Sometime). Previously at Pure Storage, set up Docker-standardized Ubuntu/Python environments to simulate hardware testbeds and support workload/performance regression testing for other engineering teams; no robotics/ROS experience.”
Sachin Reddy Kunta
Mid-Level Backend Software Engineer specializing in payments, fraud systems, and AI agent infrastructure
“Early-career engineer who owned an end-to-end objective assessment/coding contest platform at an edtech startup, using Postgres + S3 and Redis (queues + ZSET) to decouple and scale code submission processing with worker sandboxes. Also implemented idempotency controls and set up monitoring and CI/CD while the rest of the team focused on curriculum.”
Eduardo Diaz
Senior Full-Stack Software Engineer specializing in Python, FastAPI/Django, and Azure
“Backend/data engineer with production experience building real-time IoT telemetry pipelines for wind/solar assets at Siemens (FastAPI on Azure Event Hubs/Service Bus, Cosmos DB + SQL Server) and deploying GPS/fleet telematics microservices on AWS ECS Fargate with Terraform and blue/green CI/CD. Demonstrated strong reliability and performance chops, including a 30s-to-<100ms SQL optimization and owning a Kafka pipeline incident resolved in ~20 minutes.”
Akashreddy Madduri
Senior Backend Engineer specializing in real-time data platforms for FinTech and Healthcare
“Backend/data engineer with experience at JPMorgan building near real-time payment risk and fraud scoring pipelines using Python, Spark Structured Streaming, and Delta Lake, emphasizing auditability, security, and data correctness (dedupe/late events) to reduce false positives. Also led a legacy-to-cloud migration of claims/eligibility data at Cogna with parallel runs, phased rollout, and healthcare-specific validation (ICD-CPT mapping).”
jiayu Zhao
Junior Quantitative Analyst and Full-Stack Engineer specializing in FinTech and web platforms
“Backend/distributed-systems engineer with AI infrastructure experience who built an AI-driven video generation platform, focusing on an asynchronous FastAPI-based orchestration layer between user APIs and heavy inference services. Strong in production instrumentation and latency/concurrency optimization; actively learning ROS 2 but has not yet worked on physical robotics or ROS-based deployments.”
Atyab Hakeem
Junior Data Scientist / ML Engineer specializing in GenAI and computer vision
“Software engineer who built and deployed OddPulse, a multi-agent LLM-powered continuous financial auditing system aimed at reducing compliance penalties by catching issues before audit cycles. Experienced with TrueAI-based agent orchestration, Airflow on GCP batch workflows, and rigorous evaluation/benchmarking (hit rate/MRR, latency/TTFT, cost) alongside security controls for sensitive financial data.”
Abhay Ravi Kumar
Junior Software Engineer specializing in backend, cloud DevOps, and ML/NLP
“DevOps/data-automation professional with HPE experience who has deployed containerized microservices to AWS EKS and built an end-to-end observability stack (Prometheus/Grafana/CloudWatch via Terraform), reporting zero-downtime deployments and ~40% faster incident response. Also extends Python ETL automation for procurement/operations teams (rules engine, validation, performance tuning) and bridges SAP ERP data into Power BI/Qlik dashboards through close on-site user collaboration.”
Pandari G
Mid-level Machine Learning Engineer specializing in Generative AI and RAG systems
“GenAI/LLM engineer with production deployments in both fintech and retail: built an AI-powered mortgage document analysis/automated underwriting pipeline at Fannie Mae (OCR + custom LLM) cutting underwriting review from 3–4 hours to under an hour with privacy-by-design controls. Also helped build Sephora’s GenAI product advisory bot using LangChain-orchestrated RAG (Azure GPT-4, Azure AI Search, MySQL HeatWave vector search), focusing on grounding, evaluation, and compliance-aware architecture choices.”
Sarthak Vinayaka
Mid-Level Full-Stack Software Engineer specializing in enterprise AI, data pipelines, and scalable APIs
“Forward-deployed engineer/tech lead who built an end-to-end demand planning and forecasting application for a major US steel manufacturer, integrating Snowflake data into the C3 platform with batch/MapReduce workflows, monitoring, and a React/TypeScript UI. Also productionized an enterprise LLM integration with structured outputs and authorization guardrails, reporting +30% stakeholder engagement and broad adoption across customer deployments.”
Shram Kadia
Mid-level Software Engineer specializing in backend systems, cloud-native apps, and AI platforms
“Backend/full-stack engineer who has owned production systems end-to-end, including a Dockerized Node.js/TypeScript probabilistic fault-tree analysis service for nuclear safety research deployed on AWS. Also built and operated a FastAPI-based RAG pipeline over 200+ PDFs using FAISS, focusing on low-latency, idempotent workflows and strong observability; experienced with API design and Playwright E2E automation across React/Angular projects.”
Mathew Martin
Mid-level Software Engineer specializing in cloud data platforms and LLM applications
“LLM/agent builder with experience shipping production LLM features at an early-stage ed-tech mental wellness startup (conversation analysis + structured feedback via FastAPI, OpenAI API, Render, CI/CD). Also built a multi-step dining concierge agent using OpenSearch over Yelp data with fallback query relaxation, and has enterprise data engineering experience at Capgemini migrating databases to Snowflake with robust ETL normalization and data-quality handling.”
Yash Rangucha
Mid-level Software Engineer specializing in backend microservices and real-time streaming
“Built and owned an end-to-end LLM-powered enterprise retrieval pipeline at ServiceNow, spanning ingestion of structured/semi-structured sources through vector retrieval and real-time API serving. Focused heavily on reliability and quality (multi-stage validation, monitoring, evaluation pipelines) while also driving performance improvements (~35% faster responses) via caching, async processing, and SQL/query optimization.”
Jamie Cook
Senior Machine Learning Engineer specializing in AI search and recommendation systems
“Built internal production LLM tools for engineering and support, including a customer-health assistant and a RAG-based incident explainer grounded in logs, metrics, and deploy data. Stands out for combining strong GenAI safety/evaluation practices with pragmatic backend engineering, delivering measurable impact like a 40% drop in data-help requests and answers in seconds instead of minutes or hours.”
Vamsi Reddy
Mid-level AI/ML Engineer specializing in healthcare and financial ML systems
“ML/AI engineer with hands-on experience shipping both predictive healthcare models and clinical GenAI assistants into production. They combine strong MLOps depth across Azure and AWS with healthcare-specific safety thinking, including PHI guardrails, retrieval grounding, and production monitoring, and they also built internal Python tooling for fraud ML workflows at Capital One.”
Animan Khandelwal
Entry-level ML Engineer specializing in multimodal AI and healthcare applications
“Backend/ML engineer who built and operated a production WhatsApp assistant end-to-end using a modern RAG stack, delivering >90% automation with sub-2-second latency. Shows strong depth in retrieval quality, observability, evaluation, and incident handling, and has also applied similar AI workflow patterns to a clinical diagnostic assistant processing medical PDFs.”
Venkata Revanth Vardineni
Entry-level Software Developer specializing in full-stack and AI systems
“Currently at Berryble AI, this candidate is building an LLM-based real-time interview analysis engine using FastAPI, WebSockets, fine-tuned models, and GCP/Cloud Run. They stand out for using AI and agent workflows pragmatically to accelerate development while keeping human ownership over architecture, security, reliability, and maintainability, and they are also pursuing a master's in applied machine learning.”
Hari Shankar Raghuraman
Mid-level AI/ML Engineer specializing in scalable ML, NLP, and MLOps
“ML/AI engineer with strong production depth across classical ML, MLOps, LLM/RAG, and scalable Python data platforms, with experience at Cisco and Accenture. Stands out for tying technical decisions to measurable business outcomes, including $1.2M annual savings, 40% faster support resolution, and broad internal adoption of shared engineering frameworks.”
sri venkata naga jahnavi Bandla
Mid-level AI/ML Engineer specializing in cybersecurity and fraud analytics
“AI/ML engineer with production experience across both classical ML and Generative AI, including a real-time banking fraud detection platform at Deloitte and a RAG-based cybersecurity threat analysis feature at Accenture. Stands out for owning systems end-to-end—from feature pipelines and model tuning through deployment, monitoring, retraining, and API/platform reliability—with measurable impact on fraud accuracy, false positives, and SOC analyst efficiency.”
Wilson Arockiasamy
Principal Software Engineer specializing in real-time streaming and cloud-native data platforms
“Built and shipped a production LLM feature that converts natural-language search requests into Lucene queries for OpenSearch-backed device event data, improving usability for non-technical users. Brings hands-on experience across the full stack of agentic systems: model training, FastAPI/React integration, Kubernetes deployment on AWS, event-driven orchestration with NATS/Kafka, and production-grade evaluation/observability.”
venu madhav
Mid-level AI/ML Engineer specializing in Generative AI and MLOps
“AI engineer and current tech lead building a RAG-based multi-agent QA platform for financial document analysis at significant scale (40,000-50,000 documents). They combine Python, CrewAI, FastAPI, Hugging Face embeddings, Pinecone, and AWS SageMaker to deliver retrieval, calculation, summarization, forecasting, and visualization workflows, while leading a small cross-functional team.”