Vetted Amazon EKS Professionals

“Engineer working on high-throughput financial systems who uses AI pragmatically to accelerate development without sacrificing design ownership, correctness, or compliance. Particularly interesting for teams building regulated, real-time platforms: they have hands-on experience integrating fraud detection models into microservices, handling transaction ingestion, scoring, decisioning, and throughput-sensitive asynchronous workflows.”

Yash Priyadarshi

Screened

Junior Software Engineer specializing in distributed systems and cloud infrastructure

Bengaluru, India2y exp

“Backend/distributed-systems engineer who built a Golang distributed key-value store on AWS using Multi-Paxos, WAL, and non-blocking gRPC replication (cutting write latency ~40%) and proactively addressed tricky failure modes like leader-election livelock. Also developed a Python/Kubernetes cost-optimization scaling engine deployed with Helm/Terraform, delivering ~$40K annual savings while sustaining 99.99% uptime, and drives contract-first API development (OpenAPI/Swagger) to speed frontend integration.”

Namrata Shivshankar patil

Screened

Mid-level Backend/Full-Stack Engineer specializing in cloud, AI, and distributed systems

Arlington, TX6y exp

“Built and shipped internal AI support systems spanning Angular/TypeScript frontends, Java/Spring/AWS backends, and Claude-powered troubleshooting workflows. Stands out for combining full-stack product delivery with practical LLM engineering, including RAG, structured outputs, production evals, and careful human-in-the-loop safety decisions. Has shipped systems serving 150-800 daily sessions at 99.5% availability while reducing repetitive support burden.”

“Java/Spring Boot engineer with startup-style ownership experience across e-commerce, banking, and healthcare analytics. Stands out for driving a monolith-to-microservices migration with Kafka that improved checkout reliability under peak load, while also contributing full-stack with Angular and supporting production operations end to end.”

“Financial-services software engineer with experience building both full-stack advisor tools and production AI features at Charles Schwab. They owned an automated portfolio rebalancing workflow and helped launch an LLM-powered reporting assistant that cut report-writing time by 70%, while maintaining compliance through strict tool use, evals, and mandatory human review.”

“Backend-leaning full-stack engineer with 7 years of Python experience who has worked on data-heavy products in both healthcare and social media intelligence. Particularly notable for driving Elasticsearch-based search improvements on a B2B social media analytics platform and for building secure healthcare APIs using Flask/Django with OAuth, JWT, and multiple databases.”

“ML/LLM practitioner who has shipped production RAG systems (summarization + Q&A) and end-to-end Airflow-orchestrated demand forecasting pipelines at NEON IT. Strong focus on reliability—uses evaluation scripts, retrieval/chunking tuning, validation/retries/alerts, and stakeholder-driven iteration to make AI workflows consistent and usable.”

“Small-team builder of Promethium’s “Mantra” next-gen agentic text-to-SQL engine, using vector DB + LangGraph tooling and SQL validation/evaluation to improve query accuracy. Experienced in diagnosing production LLM workflow failures via LangSmith traces and in running hands-on developer workshops and pre-sales POCs with live debugging and real customer data.”

“Maintained and improved a set of JavaScript packages at JPMorgan, including a major refactor of an event-emission layer used in LLM streaming that delivered ~20% runtime speedup with no API changes. Known for a measurement-driven performance approach (profiling + simplification + validation), strong backward-compatibility discipline, and documentation that accelerates internal adoption and reduces support questions.”

“Built production LLM systems including a real-time customer feedback analysis and workflow automation platform using RAG and multi-agent orchestration with confidence-based human escalation, addressing privacy and legacy integration challenges. Also automated ML operations with Airflow/Kubernetes (e.g., daily churn model retraining) cutting retraining time to under 30 minutes, and demonstrates a rigorous testing/monitoring approach plus strong non-technical stakeholder collaboration.”

SNEHA KUSUMA

Screened

Mid-level Java Full-Stack Developer specializing in banking and telecom platforms

Dallas, TX5y exp

“Frontend-focused engineer with experience at T-Mobile and U.S. Bank who maintained a TypeScript utility library (types, tests, build pipeline, and docs) adopted by multiple teams, and improved React workflow performance by refactoring components and optimizing data fetching. Known for pragmatic cross-team support—reproducing issues quickly, shipping well-tested fixes, and managing changes carefully to avoid breaking downstream apps.”

Rishitha reddy katamareddy

Screened

Mid-level Generative AI & Machine Learning Engineer specializing in agentic LLM systems

USA4y exp

“Built and deployed a production agentic LLM knowledge assistant that answers complex questions over internal documents, APIs, and databases using a RAG architecture (FAISS/Pinecone) and LangChain/LangGraph orchestration. Emphasizes production-grade reliability and hallucination control through grounding, confidence thresholds, validation, retries/fallbacks, and full observability (logging/metrics/traces) with continuous evaluation and feedback loops.”

Sanket Dumbare

Screened

Mid-Level Software Engineer specializing in cloud-native microservices and FinTech platforms

USA3y exp

“Backend/platform engineer who led an end-to-end Python (FastAPI) transaction analytics microservice for real-time financial monitoring, including SQS ingestion, scoring/aggregation, and low-latency APIs. Strong AWS + Kubernetes/GitOps background (EKS, ArgoCD, Jenkins, ECS/ECR, CloudWatch) with hands-on experience scaling event-driven systems and executing phased on-prem to AWS migrations.”

Daniel Jin

Screened

Intern Site Reliability Engineer specializing in Kubernetes, AWS, and observability

New York, NY1y exp

“Backend/data engineering candidate specializing in Python/Flask services and ML-enabled systems, deploying containerized workloads on AWS ECS/EKS with strong observability (Prometheus/Grafana) and PostgreSQL performance tuning. Built multi-tenant architectures with row- and schema-level isolation and optimized a Kubernetes-based Airflow + Spark nightly ETL pipeline for an e-commerce client, improving performance by 250%+ and reliably beating morning reporting deadlines; also contributed to Apache Airflow (SQLAlchemy/PostgreSQL area).”

Navindu Jayatilake

Screened

DevOps Technical Lead specializing in Kubernetes, AWS, and platform engineering

Colombo, Sri Lanka6y exp

“Sri Lanka–based DevOps/Platform engineer focused on Kubernetes and AWS who has led real production incidents and recoveries (network-policy-induced asymmetric routing; node pool failure due to CNI upgrade). Built production CI/CD with GitHub Actions (ephemeral self-hosted runners on EKS), ArgoCD, Vault, and Terraform, and led a phased ECS-to-EKS migration including Kafka consumers and MongoDB Atlas private endpoints.”

Deeresh Gajjala

Screened

Senior Software Engineer specializing in Java/Spring Boot microservices and AWS payments systems

Dallas, TX6y exp

“Senior software engineer with Amazon experience who owned end-to-end improvements to a real-time payment authorization service, rebuilding it as a reactive Spring WebFlux microservice with saga orchestration and Kafka event streaming, deployed on AWS EKS with strong observability. Also built React+TypeScript and Node/Express full-stack workflow apps (onboarding, campaign management, admin review) and has experience shipping quickly in ambiguous startup environments while maintaining reliability and data correctness.”

Nishad Kane

Screened

Mid-level Data Scientist & AI Engineer specializing in RAG, agentic AI, and production ML

5y exp

“AI/data engineer who built a production LLM-powered schema drift detection system (LangChain/LangGraph) to catch semantic data changes before they break downstream analytics/ML. Deployed on AWS with Docker/S3 and implemented an LLM-as-a-judge evaluation framework to improve trust, reduce hallucinations, and control false positives/alert fatigue. Collaborated with non-technical risk/business analytics stakeholders at EY by delivering human-readable drift explanations that improved confidence in financial analytics dashboards.”

Prashant Salunke

Screened

Mid-Level Software Development Engineer specializing in full-stack and cloud-native systems

Chicago, IL4y exp

“Backend engineer who has shipped production LLM-powered features, including an AI-assisted developer tool on AWS (Spring Boot) and a blog platform capability using embeddings + Elasticsearch for semantic retrieval and LLM-generated summaries/recommendations. Demonstrates practical tradeoff management (quality/latency/cost), guardrails to reduce hallucinations, and evaluation-driven iteration using real user queries and observability via ELK.”

“Data engineer with Optum experience building end-to-end healthcare data pipelines for HL7/FHIR, processing millions of records daily across Kafka streaming and Databricks/Spark batch. Strong focus on data quality (schema enforcement/validations), reliability (Airflow monitoring/alerts), and analytics-ready serving in Snowflake powering Power BI/Tableau, with CI/CD via Git and Jenkins.”

Supreet Purthpli

Screened

Mid-level AI/ML Software Engineer specializing in cloud-native MLOps and FinTech

San Francisco, CA4y exp

“Software engineer with JPMorgan Chase experience delivering end-to-end fintech features (Next.js/React/Node/Postgres on AWS) and measurable performance gains. Built and productionized an AI-native credit decisioning workflow combining LLMs, vector retrieval, and a rules engine with strong governance (bias checks, auditability, human-in-loop), improving precision and cutting underwriting turnaround time by 40%.”

Ajay Desai

Screened

Mid-level Full-Stack Software Engineer specializing in FinTech and backend platforms

USA4y exp

“Built an AI-native legal research platform that automated analysis across 100,000+ dense legal documents, combining LLM workflows, async backend architecture, and conversational retrieval in production. Also brings cross-domain experience in investment-analysis agents and healthcare claims/billing systems, with a strong emphasis on reliability, deterministic orchestration, and safe handling of messy operational data.”

“Engineer focused on AI-assisted and multi-agent software development, with hands-on experience designing structured agent workflows for implementation, testing, validation, and architectural review. Stands out for treating AI as an accelerator rather than a replacement, combining practical experimentation with strong attention to engineering fundamentals and operational concerns like observability, latency, and cost.”

Sanket Mungikar

Screened

Mid-level Software Engineer specializing in distributed backend and AI analytics platforms

California, USA4y exp

“Full-stack engineer at BigCommerce who combines customer-facing deployment ownership with hands-on AI/LLM systems work. Built and launched merchant analytics and predictive inventory workflows using React, TypeScript, FastAPI, Kafka, AWS, and RAG-style architectures, and has real production experience debugging non-deterministic AI issues caused by data pipeline freshness and event-ordering problems.”

“Fullstack engineer from Prudential who built a workflow automation platform for internal service reps, combining Angular/React frontends with NestJS, GraphQL, Kafka, MongoDB, and Redis. Stands out for translating ambiguous business problems into scalable metadata-driven systems, validating architecture through hands-on POCs, and delivering a measurable 40% reduction in transaction handling time.”