Vetted Amazon EKS Professionals

“ML engineer/data scientist who built and deployed a real-time fraud detection platform at Citi on AWS SageMaker, processing 3M+ daily transactions and improving fraud response by 28%. Combines unsupervised anomaly detection (autoencoders) with ensemble models (XGBoost/Random Forest) plus Airflow/Step Functions orchestration, drift monitoring, and explainability (SHAP) to keep models reliable and compliant in production.”

“Robotics-focused full-stack engineer with hands-on ROS experience building sensor-processing and control nodes, plus a track record of debugging and optimizing real-time robot responsiveness via profiling and message-timing analysis. Uses Webots for pre-hardware validation and Docker/CI/CD to standardize deployments and catch issues early.”

“ML/LLM engineer with production experience building a RAG-based LLM support assistant (FastAPI, Redis, Kafka) with multi-layer validation and human-in-the-loop feedback loops to improve accuracy over time. Has orchestration and MLOps depth using Airflow and Kubeflow on Kubernetes (autoscaling, alerting, monitoring) and delivered measurable ops impact (40% ticket efficiency improvement) by partnering closely with customer support teams.”

“Backend engineer with NTT Data experience building Java/Spring Boot services for product-data ingestion, including Kafka-based asynchronous pipelines and Redis read-through caching. Also built a personal RAG system deployed on Google Kubernetes Service using FastAPI, LangChain, and Pinecone with multi-tenant data isolation; holds a Master’s background in Machine Learning.”

“ML/NLP practitioner with healthcare payer experience at HCSC, focused on connecting messy unstructured clinical notes to structured claims/provider data to improve fraud-analytics workflows. Has hands-on experience fine-tuning transformers in AWS SageMaker, building large-scale embedding search with FAISS, and implementing robust entity resolution using golden datasets, precision/recall calibration, and production monitoring for drift.”

“Cloud/infrastructure engineer from ASM Research supporting federal healthcare systems, operating multi-cloud (AWS/Azure/GCP) environments at ~2000-server scale. Deep hands-on experience with Terraform/Ansible IaC, PR-based governance (Atlantis), and secure CI/CD (OIDC/least privilege), with concrete incident response wins and HA/failover testing improvements. Not an IBM Power/AIX specialist but comfortable translating virtualization/partitioning and ops practices to new platforms.”

“Built a real-time telemedicine clinician dashboard and iterated post-launch by diagnosing lag via logs/metrics and optimizing DB queries/sync logic. Also shipped a production internal RAG knowledge assistant for support teams, including embeddings/vector DB, citation-only answers with abstention thresholds, and an eval loop driven by real ticket data that improved accuracy through chunking/overlap and batching optimizations.”

“Full-stack engineer focused on application-layer product work (70–75%), with production experience building real-time operational dashboards (React/TypeScript + Node/Express + WebSockets + Postgres) and measurable impact (50% reduction in data entry time). Also owned a Flask backend for a SaaS product with token auth/RBAC, versioning, observability, and performance tuning, and has operated containerized apps on AWS (EKS, RDS/Aurora, S3, API Gateway) including handling a real latency/scaling incident end-to-end.”

“Backend engineer with hands-on experience building real-time, event-driven systems at Walgreens, including a Kafka-based prescription status notification service and scalable pipelines for messy prescription/inventory data. Strong focus on reliability patterns (retries, idempotency, DLQs) and iterating based on pharmacist feedback to improve usability.”

“Backend engineer who built and owned an AI-powered financial research product end-to-end, using a typed NestJS/GraphQL backend with LangGraph-style agent routing to produce sourced, structured financial analysis. Emphasizes finance-grade correctness (Zod validation, metric registries, unit/empty-result guardrails) while keeping latency low via batching, caching, and fast token streaming, and has led incremental migrations using strangler/feature-flag/shadow traffic patterns.”

“Co-op engineer at EnFi who built and maintained a multi-tenant prompt library and LLM workflow tooling used by internal teams and external enterprise clients. Led TypeScript/React package design and standardized a typed workflow abstraction across disparate implementations (React, Go, JSON), improving reliability and developer adoption. Delivered measurable performance gains (~25% latency reduction) and owned end-to-end execution including docs, demos, debugging, and deployment.”

“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.”

“Built a production GPT-4/LangChain/Pinecone RAG “AI Copilot” at Northern Trust to automate financial report generation and analyst Q&A over internal structured (SQL warehouse) and unstructured policy data. Focused on real-world production challenges—grounding and latency—achieving major speed gains (seconds to milliseconds) via MiniLM embedding optimization and Redis caching, and implemented rigorous testing/evaluation with MLflow-backed metrics while aligning compliance and finance stakeholders for deployment.”

“Accenture engineer who built real-time smart mobility products (Verra Mobility) used by both consumers and government agencies, spanning React/TypeScript frontends and Node.js/GraphQL microservices with Kafka. Demonstrated strong delivery and reliability practices (CI/CD, feature flags, automated testing, CloudWatch observability) and achieved a ~20% GraphQL performance improvement supporting 50,000+ daily transactions, plus built an internal ops/support dashboard adopted into daily workflows.”

“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.”

“AI software engineer with experience spanning LLM/RAG production systems and regulated fintech infrastructure. Built an end-to-end natural-language-to-SQL analytics assistant (Weaviate + GPT-4 + Supabase) shipped as an API with 92% accuracy and major time savings for non-technical users, and also owned demand-forecasting and CI/CD/containerization improvements for a Bank of America core banking deployment at Infosys.”

“Full-stack engineer who built "Workstream AI," an AI-powered engineering visibility product that converts GitHub activity into real-time insights using an event-driven microservices stack (RabbitMQ/Postgres/Express) and GPT-4 with a React frontend. Previously a Founding SWE at a health & wellness startup, building data-driven user management tooling, and also delivered a real-time shuttle tracking/ride request system using Java Spring Boot/Hibernate + React; comfortable owning production deployment details (AWS EC2, DNS, SSL).”

“Backend/data platform engineer who owned a FastAPI + Kafka microservice in Verizon’s billing pipeline, handling high-volume usage ingestion/validation/enrichment with strong observability and CI/CD on AWS EKS. Demonstrated measurable performance gains (latency down to ~120–150ms; Kafka throughput +30–40%; DB CPU -25%) and led an on-prem ETL-to-AWS migration using Terraform, parallel validation, and phased cutover with zero downtime.”

“Infrastructure/DevOps engineer (Geico) focused on AWS and Kubernetes at production scale. Has hands-on experience building secure GitHub Actions CI/CD for EKS, provisioning core AWS infrastructure with Terraform/CDK, and leading end-to-end incident response with post-incident automation to prevent recurrence; no direct IBM Power/AIX/PowerHA experience.”

“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.”

“Built and deployed a production Llama 3-based RAG document Q&A system using FAISS, addressing context-window limits through chunking and keeping retrieval accurate by regularly refreshing embeddings. Has hands-on orchestration experience with LangChain and LlamaIndex for multi-step LLM workflows (including memory management) and collaborates with non-technical teams (e.g., marketing) to deliver AI solutions like recommendation systems.”

“Backend/data engineer with experience at Assurant and Capgemini, focused on reliability and performance at scale. Improved high-latency backend APIs by adding and iterating on a Redis caching layer driven by CloudWatch/monitoring metrics, and built scalable BI pipelines that normalize messy multi-source enterprise data with strong observability and error handling. Familiar with LLM/RAG architecture and practical guardrails, though has not yet shipped an LLM feature to production.”

“Backend/platform engineer with Optum experience owning a production FHIR Member Access API aligned to CMS interoperability requirements. Built and scaled Spring Boot/HAPI FHIR microservices on AWS (Docker/Kubernetes) with zero-downtime CI/CD, and operated them with strong observability (Dynatrace, logs/metrics, alerting) and incident response. Also implemented a Kafka-based FHIR bulk data pipeline with schema versioning, idempotent processing, and reliable backfills/replays.”