Vetted Kubernetes Professionals

“Built and deployed a production LLM-powered biomedical knowledge extraction pipeline that processed millions of papers to identify tools/techniques and produce a unified knowledge graph via active learning NER (Prodigy + spaCy transformers) and entity linking (Bio-tools/Wikidata). Addressed hard NLP engineering challenges like WordPiece span-offset alignment and scaled inference over ~1.5M documents using batching/caching, containerized services, async workers, and orchestration with Prefect/Airflow.”

“Backend engineer who built a containerized Flask service powering an engineering metrics dashboard by syncing GitHub and Jira data into PostgreSQL, with strong emphasis on schema design, query performance, caching, and background processing. Has hands-on experience with SaaS multi-tenancy (tenant scoping + Postgres RLS) and integrating AI/ML inference via separate model-serving services (FastAPI + TensorFlow Serving) and external APIs (OpenAI/Hugging Face/PyTorch).”

“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.”

“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.”

“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).”

“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.”

“Engineering/technology leader who spearheaded an ultra low-latency AI-CDN SaaS platform on a multi-cloud stack (AWS/Azure/Alicloud), helping transform ARHT from a boutique provider into a global SaaS solution. Built distributed engineering and follow-the-sun support teams and helped secure major enterprise clients (TD Bank, Gucci, NATO, EY) while also leading board communications and raising $6M for a public-listed company.”

“ML/GenAI engineer with recent CVS Health experience building a production RAG system over unstructured financial/research documents using LangChain, FAISS, and Pinecone, plus LoRA/PEFT fine-tuning of GPT/LLaMA for domain-aware summarization. Demonstrates strong applied MLOps and data engineering skills (Airflow/Prefect, Docker/Kubernetes, CI/CD, MLflow) and measurable impact (sub-second retrieval, ~40% better context retrieval, ~25% entity matching improvement).”

“Built a production "sentinel insight engine" to tame information overload from millions of product reviews and support transcripts, combining Azure OpenAI (GPT-3.5) zero-shot classification with a fine-tuned T5 summarizer to generate weekly actionable product insights. Demonstrated strong MLOps/production engineering by adding drift monitoring with embedding-based detection, integrating REST with legacy SOAP/queue-based CRM via FastAPI middleware, and scaling reliably on Kubernetes with HPA.”

“Engineering leader with hands-on coding time who has driven major commerce and data-platform transformations: defined goop’s omnichannel strategy, unified payments to Square, and rebuilt real-time NetSuite inventory flows plus forecasting tools. Currently reorganized engineering into Product/Data/Support teams to hit aggressive seasonal roadmaps, and led a data-lake/medallion ELT refactor feeding embedded analytics (Tinybird) with improved reliability and cost efficiency; also accelerates onboarding via AI coding tools in a serverless, event-driven architecture.”

“Software engineer with Citi Bank experience building real-time fraud validation/scoring for loan processing, spanning Spring Boot microservices and a FastAPI Python service secured with OAuth2/JWT. Delivered React/TypeScript operations dashboards and deployed containerized services via Docker/Kubernetes with Jenkins CI/CD, while also tuning databases (Oracle/Postgres) and handling high-volume latency/scaling issues using ELK, caching, and autoscaling on AWS.”

“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.”

“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.”

“Full-stack engineer who shipped a group scheduling SaaS feature with live availability updates using Next.js App Router + TypeScript, owning production reliability after launch (auth debugging, monitoring, polling/backoff tuning). Has hands-on experience with Postgres schema/index design and query optimization (EXPLAIN ANALYZE) and building durable orchestrated backend workflows with retries and idempotency.”

“Cloud/Kubernetes-focused production engineer with experience running 99.95% uptime platforms across AWS/Azure/GCP. Strong in incident response and performance troubleshooting (including a 30% MTTR reduction), and in building secure CI/CD and Terraform-based IaC for AKS/GKE microservices with robust change controls and rollback practices. Notably does not have direct IBM Power/AIX/VIOS/HMC or PowerHA/HACMP ownership.”

“Backend-leaning product engineer with DevSecOps depth who has shipped real-time, Kafka-driven data pipelines and AI-enabled customer-facing features to production on AWS. Built a Spring Boot API layer serving real-time predictions at 100K+ requests/day, improving latency by 35% and user task completion by ~25%, and delivered a React/TypeScript dashboard plus a Postgres audit/history model optimized for search and large event volumes.”

“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.”

“Data engineer with end-to-end ownership of large-scale retail and clinical data ingestion/processing on AWS, including real-time streaming and batch pipelines. Delivered measurable outcomes: 20M daily transactions processed, latency cut from 4 hours to 5 minutes, ~70% fewer failures, and 120+ pipelines running at 99.8% reliability with full audit compliance.”

“GenAI/LLM ML engineer (currently at Webprobo) building an enterprise GenAI platform with document intelligence and automation on AWS and blockchain. Has hands-on experience with RAG, LLM evaluation tooling, and orchestrating production LLM workflows with Apache Airflow, plus deep exposure to reliability challenges in globally distributed/edge deployments. Also partnered with business/marketing stakeholders at a banking client to deliver an AI-driven customer retention insights solution.”

“Backend/full-stack engineer with Blue Cross Blue Shield experience building a reactive, event-driven claims processing microservice platform on AWS (ECS, SNS/SQS) with Terraform-based IaC and strong observability (Dynatrace/CloudWatch). Demonstrated measurable production impact (32% less downtime, 24% higher processing efficiency) and deep database performance/migration expertise across MongoDB and Postgres.”

“Engineering Manager/player-coach leading Data Infrastructure, ML/DS, and AI Engineering pods who recently shipped multiple production agentic GenAI features. Built privacy-preserving LLM workflows (PII redaction via Microsoft Presidio) and drove an AI expense-approval agent from ambiguous ask to GA, cutting approval time from ~2.5 days to <4 hours with >85% accuracy. Also owned a major LLM cost overrun incident and implemented cost observability plus circuit breakers to prevent runaway agent loops.”

“Backend/AI engineer who owned a high-scale Java/Spring Boot microservice for a financial application (millions of requests/day) and led major reliability/performance fixes (including ORM/query and PostgreSQL tuning) achieving ~60% latency reduction. Also shipped application-layer LLM features for ops teams (summarization + tool-calling) with strong guardrails (PII redaction, validation, audit/feedback) and designed a state-driven agent workflow with retries, circuit breakers, and human escalation.”