Vetted AWS IAM Professionals

“Software engineer with experience at Molina Healthcare and Target, owning production features end-to-end across backend, data pipelines, and UI. Built an event-driven claims validation system (Python/Java/Spring Boot/Kafka) with strong observability, and shipped embeddings-based semantic product search with evaluation loops (CTR/top-k + human review) and guardrails like keyword-search fallback.”

“Data engineer with experience at Sony and Walmart building high-volume, near-real-time analytics and ingestion systems. Has owned end-to-end pipelines from Kafka/Spark streaming through S3/Parquet and Redshift/Looker, emphasizing data quality (Great Expectations), observability (CloudWatch/Azure Monitor), and reliability (Airflow SLAs, retries, checkpointing), including measurable performance and latency improvements.”

“Data engineer focused on fraud and banking analytics who has owned end-to-end batch + streaming pipelines at very large scale (hundreds of millions of records/day). Built robust data quality/observability layers (schema validation, anomaly detection, alerting) and delivered low-latency serving via AWS Lambda/API Gateway with DynamoDB + Redis, plus external data ingestion/scraping pipelines orchestrated in Airflow with anti-bot protections.”

“Data platform engineer focused on large-scale, real-time fraud systems, with hands-on ownership of streaming architectures using Kafka, Spark, Snowflake, and Databricks. Stands out for combining performance tuning and platform automation with LLM/RAG-based enrichment, delivering measurable gains in latency, fraud accuracy, false positives, and analyst decision speed.”

“CTO-oriented engineering leader who emphasizes building and mentoring teams while aligning technology work to real business needs through close collaboration with product stakeholders. Also has entrepreneurial exposure, including an alcohol-related startup and prior experience securing funding.”

“Candidate takes a disciplined, developer-in-the-loop approach to AI-assisted coding, using AI primarily for brainstorming, suggestions, and optimization while retaining full ownership of architecture and final code decisions. They also actively stay current on AI developments through research papers, communities, and emerging tools.”

“Backend engineer with end-to-end ownership experience on a real-time AI-driven payment authorization/orchestration platform at PayPal. They describe strong fintech systems depth across Java/Spring/Kafka microservices, database and latency optimization, and reliability engineering, with concrete impact including 35% fewer processing failures, latency reduced from 420ms to 140ms, 1,200+ weekly manual reviews eliminated, and 40% faster incident response.”

“Founding engineer who has built web products end-to-end in startup settings, spanning FastAPI/React application development, auth, cloud deployment, and Kubernetes-based scaling. Particularly notable for designing custom GPU autoscaling for an AI-style recommendation product and later shipping workflow-driven healthcare support tooling using Temporal, Postgres, and modular backend logic.”

“Candidate brings practical GenAI engineering experience with a disciplined approach to AI-assisted development. They have designed lightweight multi-agent workflows for a RAG-based support copilot, including retrieval, relevance validation, response generation, and groundedness checks to reduce hallucinations.”

“AI/ML engineer currently at BlackRock who deployed an AI-powered sentiment analysis microservice into a task management platform to prioritize and escalate high-risk/frustrated tickets from free-text comments. Experienced running production microservices on AWS EKS with Docker/Kubernetes/Helm and provisioning infrastructure via Terraform, with strong MLOps rigor (MLflow evaluation pipelines, canary rollouts, and real-time monitoring) and cross-functional collaboration with product/operations.”

“Full-stack engineer with primary depth in .NET Core and Python who has built and deployed end-to-end AWS applications (Lambda, API Gateway, S3, CloudFront) and supported them in production. Experienced in scaling large, data-driven workloads using queues/background workers, batching, and database tuning, with strong focus on API contracts, observability, and resilience patterns; also has hands-on React/TypeScript and some Spring Boot exposure.”

“GenAI Engineer at Infosys who built and deployed a production multi-agent RAG system for a top-tier bank, scaling to ~50,000 queries/day with 99.9% uptime. Drove measurable gains (45% accuracy improvement, 30% API cost reduction) through open-source LLM fine-tuning, Pinecone indexing/retrieval optimization, and AWS-based MLOps/monitoring, and has experience enabling adoption via developer workshops and customer-facing collaboration.”

“Backend-focused engineer who built a Python/Flask task-management API with JWT/RBAC, modular service/repository architecture, and PostgreSQL/SQLAlchemy performance optimizations (indexes, lazy loading, bulk ops, pooling). Also implemented multi-tenant data isolation strategies and built an OpenAI-powered document summarization workflow using chunking, async processing, Redis background workers, and caching to improve throughput.”

“Engineering leader/CTO-type with deep experience building and scaling a vehicle routing platform at Transdev On Demand, including a nationwide rollout to 22 US airports ahead of schedule. Drove engineering best practices (CI/CD, high test coverage, pair programming, automated deployments) and led a multi-tenant architectural upgrade to expand the routing engine to additional business lines and external customers.”

“AWS Solutions Architect intern who advised customers on securing a multi-tenant LLM-based SaaS, including isolation strategy tradeoffs and production guardrails against prompt injection. Has experience investigating a prompt-injection incident using logs/traces and TTP-style documentation, and designing scalable SDK/agent integrations via asynchronous worker architecture with prompt versioning.”

“Full-stack engineer with end-to-end ownership experience, including building a real-time campaign/inventory dashboard at P&G using React/TypeScript, Spring Boot, GraphQL/REST, Redis, Docker, and AWS (EC2/RDS/S3) with Prometheus/Grafana observability. Demonstrates strong performance and reliability focus (p95 tuning, caching, idempotent event-driven ingestion with DLQs/reconciliation) and has shipped MVPs in ambiguous early-stage environments.”

“Healthcare GenAI engineer who built a HIPAA-compliant, auditable RAG-based claims decision support system at Molina Healthcare, processing 3M claims and delivering major impact (48% faster manual reviews, 43% higher decision accuracy). Deep hands-on experience with LangChain orchestration, vector search (ChromaDB/FAISS), embedding fine-tuning, and safety controls (confidence scoring, rule validation, human-in-the-loop escalation) for clinical workflows.”

“Built and productionized a Python/LLM-based pipeline at Catalyst Solutions to automate healthcare RFP processing, turning unstructured documents into validated JSON/Excel with schema validation, confidence scoring, and human-review routing. Delivered major operational impact (hours-to-minutes processing, ~60% efficiency gain; 50+ RFPs processed) and modernized legacy scripts into a staged, more reliable architecture using incremental refactoring and fallback comparisons.”

Pranav Chand

Screened

Senior AI/ML Engineer specializing in Generative AI and LLM platforms

ServiceNow, CA5y exp

“Backend engineer focused on multi-tenant enterprise AI personalization and recommendation platforms, combining ML/LLM intent extraction with deterministic policy guardrails for compliance and auditability. Has hands-on AWS experience (ECS/Lambda/DynamoDB/S3) and led a careful DynamoDB single-table migration using dual write/read, canary + feature-flag rollouts, and strong observability/security (JWT/OAuth2, RBAC, Postgres RLS).”

Akshit Modi

Screened

Mid-level AI/ML Engineer specializing in healthcare NLP and MLOps

Remote, USA5y exp

“Healthcare/clinical ML practitioner who built and productionized ClinicalBERT-based pipelines to extract and standardize oncology EHR data, improving downstream model F1 from 0.81 to 0.92 while controlling training cost via LoRA/QLoRA. Experienced orchestrating real-time AWS ETL/ML workflows (Glue, Lambda, SageMaker) and partnering with clinicians using SHAP-based interpretability, contributing to an 18% reduction in readmissions and full adoption.”

Kanaka Chalam Volety

Screened

Staff DevOps/SRE Engineer specializing in AWS, Kubernetes, and GitOps

San Jose, CA24y exp

“Infrastructure-focused engineer with Vonage experience modernizing early-stage cloud architecture (Terraform modularization, blue-green deployments, containerization, and zero-downtime database migration planning to Aurora). Also built a local end-to-end side project, Vastu AI, combining a custom-trained YOLO model (Roboflow-labeled data) with a locally hosted LLM via Ollama to generate a vastu compliance report from floor-plan images.”

Naga Manasa Bandaru

Screened

Mid-level Frontend Software Engineer specializing in React, Next.js, and TypeScript

Dallas – Fort Worth, TX4y exp

“Product-focused full-stack engineer with FedEx experience building an internal logistics dashboard for near real-time shipment status and performance metrics using Next.js App Router + TypeScript. Strong in production ownership and performance work—uses React Profiler/Chrome DevTools to eliminate expensive re-renders and applies Postgres indexing/query tuning validated via EXPLAIN ANALYZE to improve dashboard responsiveness.”

Aditya Jaiswal

Screened

Intern Software Engineer specializing in cloud, DevOps, and applied AI

Carlsbad, CA1y exp

“Full-stack engineer with startup ownership experience (Aiir) building 15+ TypeScript/Go microservice APIs on GCP Cloud Run with Kafka-based async event streaming and React CRM integrations for billing/analytics. Strong post-launch operator who tuned Oracle performance (partitioning/indexing/query optimization) and validated a 23% retrieval-time reduction via AWR, and has a quality/DevSecOps mindset (94% Pytest coverage, GitHub Actions, SonarQube, Twistlock, CloudWatch) including migrating 18+ production CI/CD pipelines.”

Prasannakumar B Vardi

Screened

Senior Software Engineer specializing in low-latency ad targeting and distributed backend systems

Santa Clara, CA9y exp

“Backend/platform engineer who built a high-scale audience segmentation and real-time targeting system using Spark/Glue + S3/Hudi and low-latency API services backed by Redis/relational stores. Demonstrates strong production rigor: Spark performance tuning to eliminate OOM failures, API idempotency/caching to cut p95 latency ~40%, and careful dual-run/feature-flag migrations with reconciliation and rollback runbooks. Experienced implementing layered security with JWT/OAuth, RBAC/ABAC, and database row-level security to prevent privilege escalation.”