Vetted AWS IAM Professionals

Manaswini Gogineni

Screened ReferencesStrong rec.

Mid-Level Software Engineer specializing in cloud infrastructure and full-stack web development

San Francisco, CA2y exp

“Backend engineer at Electric Hydrogen who built a serverless device-log ingestion and processing platform in Python/Flask, scaling throughput (4x peak ingestion) while keeping sub-300ms API latency. Strong in Postgres/SQLAlchemy performance (partitioning, materialized views) and production ML integration (ONNX model served via FastAPI microservice with async batch inference, Redis feature caching, and drift monitoring via S3/Lambda). Experienced designing secure multi-tenant systems with schema-per-tenant isolation and KMS-backed encryption.”

Mitul Sheth

Screened

Senior Engineering Manager specializing in cloud security and graph-based data platforms

Seattle, WA9y exp

“Engineering leader at Sysdig Secure who pitched and prototyped a model data platform that initially got rejected, then proved value by migrating the CIEM offering and expanding adoption across multiple verticals. Now owns the CIEM suite plus the broader Sysdig Secure data and reporting platforms, manages 14 direct reports, and also leads a pilot AI team while remaining hands-on weekly.”

“Senior engineering leader at AWS currently managing two services in the MSK organization, including Replicator for disaster recovery/data distribution and Glue Schema Registry. He combines hands-on distributed systems depth with large-scale people leadership, having managed teams up to 32 engineers and coordinated platform onboarding work across roughly 340 AWS services. Earlier experience in payroll and compliance platforms adds strong enterprise domain breadth beyond cloud infrastructure.”

“Backend/full-stack engineer (Amazon experience) who built an AWS-based integration testing platform using Flask, ECS, Docker, and CloudWatch—cutting 1000+ test cases from ~5 hours to ~30 minutes while improving log visibility for non-engineering users. Also led a zero-downtime EU region migration with rigorous ORR testing, and built a Kinesis/Firehose/S3 + Glue/Spark replay mechanism for resilient data recovery. Side project: reproducible, cost-efficient LLM hosting platform on EKS using CDK and Karpenter for scale-to-zero.”

Nikhil Reddy

Screened

Mid-level AI/ML Engineer specializing in GPU inference and LLM platforms

San Francisco, CA5y exp

“Built and deployed an LLM-powered platform that turns models into scalable REST/gRPC APIs, focusing on keeping GPU-backed inference fast and stable during traffic spikes. Experienced with AWS orchestration (EKS/ECS/Step Functions), safe model rollouts, and production-grade monitoring/testing for reliable AI agents and workflows.”

Krishna Reddy

Screened

Mid-level AI/ML Engineer specializing in fraud detection and clinical LLM assistants

New York, NY6y exp

“Built and deployed a production clinical support LLM assistant at Mayo Clinic using a LangChain-orchestrated RAG architecture (Llama 2/PaLM) over de-identified clinical records, integrating BigQuery with Pinecone for semantic retrieval. Focused on healthcare-critical reliability by reducing hallucinations through grounding, implementing HIPAA-aligned privacy controls (Cloud DLP, VPC Service Controls), and running structured evaluations with clinician feedback.”

Kunj Amrutbhai Patel

Screened

Mid-level Software Engineer specializing in distributed backend systems on AWS

Seattle, WA4y exp

“Built production systems in the AWS ecosystem, including an internal AI assistant for diagnosing account transfer and permissions issues and an end-to-end account transfer workflow used by enterprise customers. Stands out for combining LLM/RAG design with strong distributed systems reliability practices, emphasizing guardrails, fallbacks, and operational trust in high-stakes workflows.”

kartik kanotra

Screened

Mid-level Software Developer specializing in cloud data engineering and MLOps

NYC, New York3y exp

“Software engineer with strong AWS production experience, including an end-to-end historical backfill system exporting ~10PB of CloudWatch logs into a data lake using Step Functions/Kinesis/Lambda/Firehose/Glue. Emphasizes reliability and operability (DynamoDB checkpointing, monitoring dashboards, CI/CD with canary tests) and has also built customer-facing UI work for the Visa Developer Portal using Angular + Spring Boot, plus React/Redux frontend work.”