Vetted Amazon ECS Professionals

“Built and shipped an AI-powered driver ranking/assignment system at AffirmoAI using LLM intent classification + RAG over pgvector/Postgres, served via FastAPI with a React UI that explains scores. Drove measurable improvements through optimization and iteration (latency down to <800ms, adoption 60%→90%+) and implemented rigorous eval loops with dispatcher ground truth plus cold-start handling for new drivers.”

Sowmya Kasu

Screened

Mid-Level Software Engineer specializing in Healthcare IT and cloud-native microservices

Northridge, CA6y exp

“Backend/ML engineer with healthcare experience at Kaiser Permanente building HIPAA-compliant Java/Spring Boot + GraphQL APIs integrated with Epic HealthConnect, including hands-on reliability/performance debugging using Prometheus/Grafana and resolver-level N+1 elimination. Also built an end-to-end malaria parasite detection ML feature (CNN/R-CNN) with evaluation, guardrails, and workflow integration, and has experience designing robust state-machine-based automation with retries, DLQs, and alerting.”

Sai Bandaru

Screened

Mid-level Machine Learning Engineer specializing in fraud detection and LLM systems

Boston, MA6y exp

“At FiVerity, built and deployed a production LLM/RAG-based Information Gathering Tool for credit union fraud analysts that generates auditable investigation summaries from verified evidence. Focused on high-stakes constraints—hallucination prevention, cross-entity leakage controls, compliance/PII-safe monitoring, and latency—while also shipping customer-facing agentic workflows using CrewAI and LangGraph in close partnership with fraud and compliance stakeholders.”

“Backend/data engineer focused on fintech payments and fraud systems, owning real-time Kafka-based reconciliation pipelines end-to-end (~13k tx/day). Built audit-friendly validation/reconciliation (SQL + Python), kept lag to seconds, and cut errors ~20%, while also shipping Spring Boot APIs with Redis caching and strong idempotency/versioning. Has early-stage startup experience standing up payment services on AWS with Docker + GitHub Actions and production monitoring/incident handling.”

George Platon

Screened

Principal Full-Stack Engineer specializing in AI, DevOps, and cloud platforms

Romania, Romania16y exp

“Built a production end-to-end AI video-to-reels clip extraction system using a multi-agent architecture with transcription, captioning, effects generation, and centralized orchestration. Demonstrates unusually strong systems thinking around reliability, observability, evaluation, and production tradeoffs for LLM-powered workflows, including Kubernetes/Kafka-based deployment and regression-driven prompt governance.”

Kevin Delong

Screened

Senior AI/ML Engineer specializing in Generative AI, LLMs, and RAG systems

Irvine, CA12y exp

“AI/ML engineer with hands-on experience shipping production systems across fintech, travel, and legal use cases. They’ve built end-to-end chatbot, generative content, and RAG solutions on AWS with CI/CD, monitoring, and guardrails, including a loan application platform that generated $3,000 in sales in its first month.”

“Software engineer focused on backend/full-stack, distributed systems, cloud infrastructure, and AI-related work. Stands out for using AI and multi-agent workflows as an engineering accelerator while maintaining rigorous testing, logging, and system-level validation, including work on telemetry and monitoring platforms where reliability and correctness are critical.”

“LLM/agentic-systems practitioner who has repeatedly taken LLM-driven pricing/decision services from prototype to production using pilots, guardrails, observability, and staged rollouts. Demonstrates strong real-time incident troubleshooting (dependency timeouts, cached fallbacks) and post-incident hardening (isolation/async/alerts), and also supports go-to-market via developer workshops, technical demos, and sales-aligned POCs.”

“Backend/data engineer in healthcare who built an AWS-based clinical analytics platform from scratch (DynamoDB/S3/Airflow/dbt) with sub-second clinician query goals, 99.9% uptime, and HIPAA-grade controls (KMS encryption, IAM RBAC, audit trails). Also modernized ML delivery by replacing a manual 4-hour deployment with a 30-minute Docker/GitHub Actions CI/CD pipeline using parallel runs, parity testing, and rollback, and caught critical EHR data edge cases (date formats/timezones) that could have impacted patient care.”

“Backend engineer working on Plumas Bank’s digital modernization, building a FastAPI-based loan origination/processing system with OAuth2/JWT security, AWS Lambda-driven PDF document generation to S3, and MongoDB integration. Has led a legacy workflow migration to a new microservice using dual-write/dual-read and monitoring, and emphasizes multi-tenant isolation via layered API controls plus row-level security.”

“Backend-focused software engineer building distributed, event-driven Java/Spring Boot microservices with Kafka for low-latency, high-frequency processing. Has hands-on experience modernizing a legacy Java system into containerized microservices deployed on Kubernetes with GitHub Actions CI/CD, and has integrated retrieval-based AI components into production workflows; no ROS/robot hardware experience yet.”

“Backend/platform engineer with hands-on ownership of Kubernetes GitOps delivery (GitHub Actions + Argo CD) on AWS EKS, including progressive rollouts and reliable rollback across interdependent microservices. Built a Python/FastAPI ML-driven document-processing service (PostgreSQL + S3) to complement existing Spring Boot systems, and implemented Kafka streaming pipelines with Schema Registry plus Prometheus/Grafana observability. Also supported a hybrid cloud-to-on-prem migration for compliance/latency with phased rollout and incremental PostgreSQL migration.”

“Cloud/hybrid infrastructure engineer with McKesson experience migrating tightly coupled healthcare applications to microservices on AWS EKS. Strong in IaC-driven standardization, CI/CD automation, and production observability (CloudWatch/Splunk/Prometheus/tracing), with demonstrated ability to debug complex incidents spanning Kubernetes and cloud networking.”

“Built and deployed a production LLM/RAG system at CVS to automate clinical documents, addressing PHI compliance, retrieval accuracy, and latency; achieved a 35–40% reduction in review effort through chunking and FP16/INT8 optimization. Also has experience translating AI outputs into actionable insights for non-technical stakeholders (sports analysts).”

“ML/AI engineer with production experience in high-scale banking fraud detection at Truist, building an end-to-end pipeline (Airflow/AWS Glue/Snowflake, PyTorch/sklearn) with automated retraining and Kubernetes-based deployment; delivered measurable gains (22% fewer false positives, 15% higher recall) and reduced manual ops ~40%. Also partnered with clinicians at Kellton to deploy an LLM system for summarizing/classifying clinical notes, improving review time and decision speed.”

“Senior technologist (25 years experience) who served as chief architect/CTO for a patented software startup that was acquired. Strong at building scalable, robust, technology-agnostic systems and translating technical value into investor-ready narratives (forecasts, roadmaps, documentation). Currently prefers joining an existing founding team as a key technical leader/mentor rather than leading entrepreneurship solo.”

prashansa chaudhary

Screened

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

Redwood City, CA4y exp

“Software engineer who has owned customer-facing/internal platforms end-to-end, emphasizing fast iteration through small releases backed by monitoring and rollback safety. Built SurveyAI with reusable React/TypeScript components and a stateless Node.js REST backend with clear API contracts/validation, and created an internal Airflow + AWS Lambda automation tool integrated with Slack alerts to reduce manual work and improve response time.”

Prasad Krishna

Screened

Mid-level Full-Stack Developer specializing in healthcare analytics and microservices

Remote, USA4y exp

“Built and maintained an air-quality prediction backend in Python/Flask that serves offline-trained ML models to a React dashboard via JSON REST APIs. Demonstrates strong performance focus across the stack—low-latency inference under load, SQLAlchemy/Postgres query optimization, multi-tenant data isolation, and caching/background task strategies for high-throughput systems.”

makarand pundlik

Screened

Mid-Level Software Engineer specializing in AWS serverless and Node.js microservices

New York, NY2y exp

“Software intern at BestWork who owned an AI-powered sales performance chatbot end-to-end: React/Material UI frontend, TypeScript AWS Lambda backend, and AWS Bedrock (Llama 3) + OpenSearch knowledge base over Salesforce/HubSpot data with Slack-based weekly summaries. Worked directly with the CTO in a high-ambiguity environment, including building an audio bot from scratch just in time for a client demo, and implemented metadata-based retrieval to handle multi-team knowledge base constraints.”

Raj Patel

Screened

Junior Machine Learning Engineer specializing in LLMs and RAG systems

Remote, USA1y exp

“Production-focused applied ML/LLM engineer who has deployed an LLM-powered RAG assistant and improved reliability through rigorous retrieval evaluation (recall/MRR), reranking, and guardrails that prevent confident wrong answers. Experienced running containerized ML/LLM services on Kubernetes (including AWS-managed layers) with CI/CD and observability, and has delivered a real-time predictive maintenance system using streaming sensor data and time-series anomaly detection in close partnership with maintenance teams.”

Haritha Kuraparthi

Screened

Mid-level Full-Stack Developer specializing in cloud data engineering and analytics

West Haven, CT4y exp

“Software developer with hands-on experience owning customer-facing work end-to-end (requirements, implementation, testing, and feedback-driven iteration) using Python and React.js. Also described remodeling an internal legacy page/tool to improve performance and accuracy, and has exposure to microservices and RabbitMQ plus ETL-based system work.”

Alicia Geng

Screened

Entry-level AI/ML Engineer specializing in AWS MLOps and computer vision

Worcester, MA0y exp

“Built and shipped a production RAG question-answering system using LangChain/OpenAI, Docker, and FastAPI, then reduced hallucinations through disciplined retrieval tuning and constrained prompting. Also implemented a custom evaluation framework (QA-pair dataset) to measure faithfulness/relevance and deployed containerized ML microservices on AWS ECS/Fargate with ALB and rolling, zero-downtime updates.”

Visaj Kapadia

Screened

Mid-Level Full-Stack Developer specializing in AWS and scalable web platforms

Santa Monica, CA5y exp

“Software engineer with hands-on AWS experience optimizing an email campaign delivery system—re-architected a monolithic worker into multi-threaded/multi-worker ECS components to boost throughput ~600% (5 to 35 emails/sec). Comfortable debugging production issues (e.g., SQS/EventBridge policy misconfiguration) and emphasizes maintainable delivery via design docs, TDD, versioned APIs, and strong test coverage.”

SREYAS GANGJI

Screened

Mid-level Software Engineer specializing in AI/ML backend systems

Chicago, IL4y exp

“AI/data engineer at ZS Associates focused on production-grade agentic systems, FastAPI microservices, and cloud-native ETL/RAG pipelines at significant scale. They’ve built multi-agent validation and diagnostic workflows inspired by their Copilot/KUBEPILOT AI work, supporting 500K+ records per day while improving ML inference performance by ~30% and cutting manual troubleshooting by 60%.”