Vetted AWS IAM Professionals

Sirisha Maddikunta

Screened

Mid-level Generative AI Engineer specializing in enterprise LLM and healthcare AI solutions

O Fallon, MO6y exp

“Built and owned an end-to-end LLM-powered fraud investigation assistant that automated case summaries and risk analysis, cutting analyst investigation/documentation time by 40%. Stands out for translating RAG concepts into a production-grade internal platform with strong evaluation, monitoring, and reusable Python service architecture that improved both analyst trust and engineering velocity.”

“Built and owned a production conversational AI platform for a healthcare contact center, including RAG-based agent assist, hybrid retrieval, safety guardrails, and production monitoring. Stands out for combining LLM product delivery with strong operational rigor, driving a reported 25-30% improvement in handling time in a sensitive healthcare environment.”

“Full-stack engineer with hands-on ownership of a customer-facing advanced performance metrics experience in the Amazon S3 console, spanning React UI, Python/Node services, Redshift/RDS data access, and AWS IaC/CI-CD with CloudWatch/Route53 operational readiness. Demonstrates strong production instincts around resilience (partial failures, multi-region inconsistencies), progressive rollouts/feature flags, and reliable ETL/integration patterns (idempotency, backfills, reconciliation).”

“Backend/platform engineer with payroll domain depth who built high-volume payroll processing microservices (Java/Spring Boot, Kafka, PostgreSQL, Redis) on AWS Kubernetes and debugged major peak-cycle latency by redesigning transaction boundaries and moving to async Kafka processing (>50% latency reduction). Also shipped an LLM-powered HR assistant using RAG with strong security/guardrails (RBAC, PII masking, audit logs) that cut support tickets by 40%, and designed reliable multi-step agent workflows with retries, circuit breakers, and idempotency.”

“Backend/LLM engineer who built a production-critical Amazon Bedrock + RAG correction and compliance layer for employee communications, integrating tightly with existing Spring Boot/AWS microservices to reduce manual review while keeping outputs explainable and auditable. Also designed an event-driven system processing 10M+ events/day (SQS/Lambda/DynamoDB/Elasticsearch) and handled on-call incidents with strong observability and reliability patterns (idempotency, retries, hotspot mitigation).”

“Infrastructure/DevOps engineer with primary ownership in enterprise Linux and AWS/Azure production environments (including financial systems). Built secure, repeatable CI/CD pipelines deploying containerized workloads to EKS/ECS and implemented Terraform/CloudFormation IaC with drift detection and rollback practices; lacks direct IBM Power/AIX/PowerHA experience.”

“Backend/ML systems engineer with Stripe payments experience who built an asynchronous processing upgrade handling millions of API requests, cutting peak latency ~20–25% while preserving strict financial consistency via idempotency-safe retries and robust validation/fallbacks. Also built scalable ETL pipelines for messy CSV/Excel/API data with strong observability (structured logging/monitoring) and reliability mechanisms.”

“Data engineer with healthcare (CVS Health) experience who migrated production PySpark workloads to native BigQuery SQL and built a Great Expectations-based validation microservice on GKE (Flask + REST) integrated into Cloud Composer. Has operated high-volume pipelines (~300–400GB/day) and designed external vendor ingestion on AWS (Lambda/Step Functions/Glue) with schema-drift detection, alerting, and backfill-safe controls to protect downstream Snowflake/BigQuery tables.”

Velusamy Ramasamy

Screened

Principal Software Architect specializing in cloud platforms, data engineering, and enterprise security

Seattle, WA29y exp

“Engineering leader with experience defining solutions from business requirements through detailed specifications and implementation, emphasizing cost-aware technology selection. Has led architectural changes including adding IBM Cloud alongside AWS for budget reasons and integrating caching/messaging to improve availability and performance, and describes scaling distributed teams via experienced DevOps/QA hires and structured evaluation.”

Shriya Bannikop

Screened

Mid-level Software Engineer specializing in cloud platforms, data engineering, and distributed systems

Seattle, WA5y exp

“Full-stack engineer who built and owned an AI-assisted job-matching dashboard in Next.js App Router/TypeScript, keeping LLM logic server-side and improving performance via deduplication, caching/revalidation, and streaming (35% fewer duplicate LLM calls; 40% faster first render). Also has strong data/backend chops: designed Postgres models and optimized queries at million-record scale (1.8s to 120ms) and built durable AWS multi-region telemetry workflows with idempotency, retries, and monitoring.”

Antara Bhavsar

Screened

Mid-level Software Engineer specializing in cloud-native systems and Android development

Bloomington, IN3y exp

“Application-focused software engineer with experience at Amazon and Motorola shipping production systems ranging from developer monitoring/on-call tooling (Alcazar, ~40% MTTR improvement) to consumer AI features used by 100K+ users. Currently building an AI/ML-driven platform with a Python/FastAPI backend on AWS (ECS/RDS/S3) and has handled real production latency/scaling incidents end-to-end.”

Bhanu Chander

Screened

Senior Data Engineer specializing in cloud data platforms and real-time pipelines

New York, NY6y exp

“Data engineer focused on reliability and observability, building end-to-end pipelines processing millions of records/day from sources like S3 and Kafka. Has hands-on experience with Airflow-based data quality automation, PySpark/Databricks transformations, and shipping versioned Python REST APIs deployed via Docker/Kubernetes with CI/CD (Jenkins) and monitoring (CloudWatch/Azure Logs).”

Jignesh Desai

Screened

Director of Software Engineering specializing in fraud detection and payments platforms

San Francisco Bay Area, California, USA26y exp

“Engineering manager/player-coach who focuses on laying technical foundations and improving team execution: kickstarted Visa standard UI library adoption, standardized API exception handling, and introduced a test automation framework that enabled a shift from quarterly to monthly releases. Led a phased latency/throughput initiative that doubled throughput, and owned a production incident caused by a dependency’s expired certificate, driving monitoring and certificate-expiry notification improvements across dependent systems.”

Abraham Musa

Screened

Senior Solutions Architect specializing in cloud AI infrastructure and security

Union City, NJ9y exp

“Cloud-native architect focused primarily on AWS, with experience designing Kubernetes and AI/ML infrastructure for customers rather than owning day-to-day operations. Particularly interesting for AI platform roles: they described using Amazon Bedrock to analyze Terraform and automatically generate compliant IaC templates and runbooks for new multi-cloud AI environments.”

“Full-stack developer who built a learning management web app end-to-end using React, Spring Boot, and MySQL, integrating APIs via Axios and validating/testing with Postman. Has experience handling data-heavy workloads (courses, quiz results) and improving performance with pagination, and is comfortable designing microservice-style endpoints with CI/CD considerations.”

Gagan Reddy Konani

Screened

Mid-level Machine Learning Engineer specializing in LLMs and RAG for healthcare

Remote, USA2y exp

“AI Engineer (Medtronic) who deployed a production RAG-based clinical assistant grounded in curated biomedical literature (no patient-identifiable data). Deep hands-on experience orchestrating and hardening LLM workflows with LangChain/LangGraph, including stateful agentic flows, rigorous testing, and evaluation; reports a 72% accuracy improvement through retrieval enhancements (query rewriting, multi-query expansion, MMR reranking).”

Nicholas Moore

Screened

Senior Full-Stack Engineer specializing in scalable cloud-native systems

Lehi, Utah13y exp

“Backend/data engineer with production experience building high-concurrency customer engagement platforms at KomBea on AWS (EKS + Lambda) using FastAPI/Django, PostgreSQL, Redis, and strong observability. Has modernized legacy batch systems into modular Python services with parallel-run parity validation and phased rollouts, and has delivered resilient AWS Glue ETL pipelines with schema evolution and data quality controls.”

Geetha Sri Kasu

Screened

Mid-level Software Engineer specializing in Java/Spring Boot, Kafka, and AWS

5y exp

“Software engineer who owned an end-to-end self-service reporting workflow (secure APIs, async/batched processing, and React UI), improving report generation performance by ~30–40% and reducing manual support effort. Also built a RAG/embeddings prototype over internal docs and service logs with grounding-focused guardrails, and has a strong reliability/observability mindset (retries, DLQs, CI/CD validation, dashboards/tracing) for distributed workflows.”

Yash Nileshkumar Mirani

Screened

Mid-level Software Engineer specializing in AI agents, data pipelines, and cloud systems

Sunnyvale, CA5y exp

“Generalist software engineer with recent contract work at Vertex Pharmaceuticals shipping a desktop-integrated RAG assistant for lab scientists (2000+ pages ingested; ~40% support-ticket reduction in pilot). Previously owned Python/AWS financial automation services at Amazon operating at multi-billion-dollar scale, with strong strengths in API design, observability, and database/performance tuning; also built a React/TypeScript AI contract analysis product (ContractsGuy).”

“Platform/infrastructure-focused engineer with hands-on experience automating AWS EKS upgrades in Python, building observability around logs/metrics/SLOs, and migrating Terraform delivery from CodePipeline to Atlantis. They describe shipping automation that handled 140 resources across 50+ Terraform files ahead of schedule and debugging a Karpenter/CoreDNS regression in Kubernetes by tracing it from regional traffic spikes to a missing local DNS cache configuration.”

Yash Patel

Screened

Mid AI/ML Engineer specializing in MLOps and cloud platforms

Texas, USA3y exp

“ML/AI engineer with strong end-to-end production ownership across classical ML and GenAI systems. Built and deployed predictive analytics and RAG-based internal tools on AWS/Kubernetes with measurable impact on accuracy, latency, deployment speed, safety, and user productivity.”