Vetted Microservices Professionals

“Software engineer with experience building a Vue.js/TypeScript internal component library (with Jest testing standards) and improving JS runtime performance via profiling, code splitting, and lazy loading. Also led documentation and community support for a Python ML utility library, diagnosing metric-calculation bugs for imbalanced datasets and driving large reductions in support inquiries through targeted docs, tests, and rapid hotfixes in a startup environment.”

“Software Developer at NYU (GEMSS) focused on scaling and optimizing a data-heavy asset management web app, including migrating/optimizing data access via Google Sheets API and Firestore. Previously an SDE at Sainapse working on Spring Boot microservices POCs (Kafka, Hadoop at 2B+ record scale). Built an end-to-end Apple Wallet coupon generation/redemption system using PassKit + Google Apps Script with measurable ops impact (40% efficiency gain).”

“Backend/platform engineer who built an AI RAG system on FastAPI/Postgres/AWS with 10+ microservices, vector search optimization (ANN + two-stage re-ranking), and GitOps-driven CI/CD that cut deploy time from hours to minutes. Also deployed Java identity services on Kubernetes at TSMC for 200K+ users using ArgoCD/Azure Pipelines, and built a reliable real-time IoT pipeline (MQTT/Node/MongoDB) with strong consistency controls.”

“Built and deployed an AWS-based LLM/RAG ticket triage and knowledge retrieval system (Pinecone/FAISS + Step Functions + MLflow) that cut support resolution time by 20%. Demonstrates strong production focus on hallucination reduction, PII security, and low-latency orchestration, with measurable evaluation improvements (e.g., ~25% grounding accuracy gain via re-ranking) and proven collaboration with support operations stakeholders.”

“Red Hat ML/LLM engineer who designed and deployed a production LLM-powered customer support automation system using RAG, improving latency by 30% via PEFT and vector search optimization. Built security and governance into retrieval (access-level filtering, encrypted Pinecone/ChromaDB) and delivered SHAP-based explainability via a dashboard for non-technical stakeholders. Experienced orchestrating distributed ML/RAG pipelines across AWS SageMaker and OpenShift with Airflow/Prefect, plus multi-agent workflows using CrewAI and LangGraph.”

“Senior engineer/tech lead with 18+ years building large-scale distributed applications, specializing in performance and reliability improvements. Recently owned multiple apps on an email personalization team, shipping major optimizations (including a push-update feature and audience-count architecture redesign) that reportedly lifted system performance from ~50% to ~99% while also leading code standards, reviews, and mentoring.”

“Startup product/enterprise-stack builder who helped take Ventric Health from R&D to a market-launched healthcare product in 2024, operating in a $34M-raised, revenue-generating model leveraging Medicare C reimbursement through providers. Previously drove adoption of an integrated K-12 digital learning platform distributed to 100+ institutions over two years, with reported improvements in test scores.”

“Platform-focused engineer experienced in productionizing ML/LLM systems: containerized a local prototype, implemented CI/CD, deployed to Kubernetes with scaling controls, and added monitoring/logging. Comfortable diagnosing real-time issues in LLM/agent workflows using logs/metrics and incident stabilization tactics, and supports sales calls by clearly explaining production scalability to unblock customer decisions.”

“Infrastructure-leaning engineer who has built real-time ML systems end-to-end: a Jetson-deployed adaptive Whisper ASR service (Flask + WebSockets, React/TS UI) and a high-throughput Postgres schema for live transcription. Also delivered customer-facing AI billing/OCR improvements for a dental startup (Dentite), boosting OCR performance by 38%, and has experience instrumenting open-source ML deployment stacks to add infrastructure visibility.”

“Product-focused full-stack engineer who built and shipped subscription/billing features end-to-end (FastAPI + React/TypeScript), including a self-serve cancellation flow with save-offers that reduced support tickets and improved churn insights. Experienced operating on AWS (ECS/EC2, RDS, S3/CloudFront) and handling real production scaling incidents through DB/query optimization, idempotent API design, and strong observability practices.”

“LLM/RAG engineer who has shipped production systems in high-stakes domains (fraud analytics at Mastercard and security compliance as a CI/CD gate). Strong focus on reliability: hybrid retrieval for latency, citation-backed outputs for trust, and code-driven eval/regression pipelines using golden datasets. Also built scalable OCR-based ingestion for messy classroom artifacts (handwriting, PDFs, whiteboard photos) using Go/Python and cloud services.”

“Infrastructure reliability/capacity-focused engineer with hands-on IBM Power/AIX (LPAR/DLPAR, HMC, VIOS) performance troubleshooting and modern cloud-native delivery experience. Built production CI/CD and Terraform-managed AWS/EKS environments, and has led real incident recoveries spanning Kubernetes autoscaling and AWS quota constraints with concrete RCA and prevention improvements.”

“Backend-leaning full-stack engineer with Walmart experience building and operating high-volume media upload and processing systems. Strong in Java/Spring Boot, Postgres performance tuning (EXPLAIN/ANALYZE), and durable workflows using Kafka/Spring Batch with retries and idempotency, plus production ownership via monitoring and optimization; familiar with Next.js/TypeScript and modern React performance patterns.”

“Full-stack engineer with production ownership across React/TypeScript, Node/Express, and Postgres, including zero-downtime releases and rollbackable migrations. Demonstrated measurable performance wins (20% response-time reduction) through DB query profiling and batching, plus hands-on AWS operations (ECS/Lambda/CloudWatch) and reliability patterns for ETL (retries, DLQs, idempotency). Experience shipping microservices quickly in ambiguous, fast-paced environments (Deloitte).”

“Full-stack engineer with recent JPMorgan experience building GPT-4-powered customer sentiment/feedback analytics products (Next.js 14 App Router + FastAPI + Postgres) and owning them post-launch with CloudWatch/Datadog observability. Also implemented Temporal-based transaction reconciliation workflows with strong reliability patterns (idempotency, retries, DLQ, versioning) and has prior high-scale healthcare dashboard experience at Optum.”

“Player-coach engineering leader focused on buyer/distribution product lines, building scalable purchasing/planning frameworks and modernizing workflows. Drove performance and reliability improvements via queue-based async architectures, external API redundancy, and CI/CD automation, and has led production incident response (cache-related) with follow-up playbooks and monitoring. Experienced in high-growth/startup environments, combining hands-on delivery with mentoring, 1:1s, and performance coaching.”

“Backend/platform engineer who has built and run production Python/Flask + Kafka microservices processing RFID and camera/RFID fusion streams for near-real-time retail cart updates at ~4–5M events/day. Strong in reliability/performance debugging (p99 latency, Kafka lag, Cosmos DB RU hot partitions) with measurable impact including ~30% database cost reduction, and has also shipped an end-to-end vulnerability scanning workflow with DynamoDB-backed state, idempotency, and robust retry/verification guardrails.”

“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.”

“Built and deployed a production LLM-driven Dungeons & Dragons game where the model acts as a dungeon master, adding a structured combat system and a macro-state tree to ensure campaigns converge to a clear ending. Fine-tuned Gemini 2.5 Flash on Vertex AI and deployed on GCP with Kubernetes, using RAG over DnD rules/spells plus multi-agent orchestration (intent-based routing between narrative and combat agents) to reduce hallucinations and improve reliability.”

“Backend engineer with hands-on experience building a fraud-transaction monitoring system in Python/Flask, architected as Dockerized microservices and integrated with Kafka for high-volume streaming. Demonstrates strong performance and reliability chops across PostgreSQL/SQLAlchemy tuning (EXPLAIN ANALYZE, N+1 fixes, bulk ops), multi-tenant data isolation, and scaling via background workers + Redis caching, plus real-time ML inference deployment using TensorFlow on AWS.”

“AI/ML engineer who has built a production text-to-image generation system in PyTorch with an AWS-backed inference setup, focusing on GPU-efficient training and embedding-space architectural choices inspired by recent research (e.g., Meta VL-JEPA). Uses both metric-based evaluation (FID) and human testing to validate real-world visual quality, and can translate technical concepts for non-technical stakeholders.”

“LLM/agentic workflow engineer with hands-on experience turning demo-grade LLM analytics into production-ready features by tackling tail latency, observability, and cost/reliability controls. Strong at diagnosing real-time customer incidents via trace-driven debugging across retrieval, tool calls, retries, and prompt/version metadata, and frequently partners with sales as a technical lead to de-risk enterprise pilots through transparent failure-mode walkthroughs.”