Vetted GitLab CI Professionals

“Enterprise platform engineering leader who identified platform engineering as a major opportunity at Kyndryl and built an entire internal practice around it by codifying the offering and evangelizing it across leadership. Now exploring founding an agentic AI developer platform aimed at reducing variance and improving consistency in building/deploying cloud-native applications; has not raised capital yet.”

“Built a production AI-driven contract/document extraction system combining OCR, normalization, and LLM schema-guided extraction, orchestrated with PySpark and Azure Data Factory and loaded into PostgreSQL for analytics. Emphasizes reliability at scale—using strict JSON schemas, confidence scoring, targeted retries, and multi-layer validation to control hallucinations while processing thousands of PDFs per hour—and partners closely with non-technical business teams to refine fields and deliver usable dashboards.”

“Backend/microservices engineer who built a Python (Flask/MySQL) data-processing microservice for an internal analytics platform and improved slow responses via query optimization and caching. Has hands-on Kubernetes experience on AWS EKS with GitLab CI/CD, plus GitOps workflows using Helm and ArgoCD. Also built a real-time Kafka order-event pipeline and supported a cloud-to-on-prem migration with standardized, containerized configuration and gradual traffic cutover.”

“Backend engineer who has delivered large, measurable performance wins (10x throughput, 67% latency reduction) by combining Flask microservices, Redis caching, and AWS autoscaling/observability. Has hands-on depth in SQLAlchemy/Postgres optimization and production scaling pitfalls (cache consistency, connection exhaustion), plus experience deploying real-time ML inference (XGBoost) on AWS Lambda and building secure multi-tenant Kubernetes isolation.”

“ML/NLP engineer who built a production system that converts large-scale unstructured text into a connected, searchable knowledge base using spaCy + Sentence Transformers/FAISS and a Neo4j knowledge graph, with BERTopic and XGBoost for organization/labeling. Strong focus on production-grade Python workflows (FastAPI/Celery, Pydantic validation, Docker, AWS ECS/Lambda) and robust entity resolution with measurable precision/recall and human review for low-confidence matches.”

“QA tester with end-to-end ownership of feature/module quality across the full development lifecycle (kickoff through release validation), using Jira/TestRail and disciplined triage workflows. Cites catching a critical data mismatch before release and a reproducible HUD/UI update defect supported by video and system logs; has not yet shipped a AAA title but has comparable production QA processes.”

“QA engineer with 4+ years testing UX/UI for enterprise, data-driven and legacy platforms (including Dell Technologies), partnering closely with developers/product/business through Agile sprints. Experienced validating end-to-end behavior across UI, REST APIs (Postman), and databases (SQL), and automating regression with Selenium Java/TestNG. Notable work includes diagnosing search ranking/pagination issues by correlating UI behavior with API responses and metadata consistency.”

“Infrastructure/DevOps engineer (Vidmob) focused on AWS + containers, owning GitLab CI/CD and Terraform-managed environments. Led a high-impact CI incident by correlating runner queue time, Docker pull latency, and NAT egress; implemented ECR pull-through caching and VPC endpoints to restore performance and then standardized the fix in Terraform for future scale-ups.”

“QA Automation Engineer with 7+ years building dependable enterprise automation suites across UI, API, and database layers using Selenium (Java), Playwright, Karate, and Cypress. Integrates smoke/regression suites into CI/CD (GitLab/Jenkins/GitHub Actions) with reporting and notifications, and has prevented production issues by catching silent backend failures and high-impact payment defects through end-to-end validation and strong root-cause evidence.”

“Backend-focused engineer with production experience building Spring Boot services for automated workflow and data-processing platforms, using queues plus retry and idempotency patterns. Also uses Python to automate data processing; emphasizes testing and peer review for maintainability.”

“QA professional from enterprise SaaS/cloud environments (Exeter; previously Boeing programs) with strong UI and end-to-end workflow testing experience. Known for rigorous defect investigation and validation across UI/API/DB layers (including SQL checks) and disciplined tooling/workflow in Jira/Azure DevOps/ALM Octane; has QA lead experience supporting release readiness and go/no-go decisions. No professional game studio/AAA shipping experience yet, but experience maps to complex, high-pressure release cycles.”

“Backend/platform engineer who has owned end-to-end production systems in financial/claims domains, including a transaction analytics microservice platform processing ~10M daily operations and cutting latency from ~150ms to <70ms. Also productionized an LLM-powered monitoring/alerting capability (Llama 3 + FastAPI) with prompt design, guardrails, and production evaluation, and led monolith-to-microservices modernization on AWS using feature flags and parallel runs.”

“Data/analytics-focused engineer who builds end-to-end KPI reporting and validation products used daily by plant leads and leadership to track yield, downtime, and defects. Combines Python/SQL + Power BI data pipelines with strong data-quality practices (automated validation, monitoring/alerts) and has experience designing scalable frontend architecture in TypeScript/React and working in distributed/microservices-style data systems.”

“Full-stack engineer who built an internal user-activity tracking and reporting system end-to-end using React/TypeScript, Node/Express, and Postgres, deployed on AWS (EC2/ALB, S3/CloudFront) with CloudWatch observability. Emphasizes reliability and data correctness via idempotent ingestion, retries with exponential backoff, backfills/reconciliation, and performance tuning as data scales, and has experience shipping quickly in ambiguous early-stage startup conditions.”

“Backend/data engineer with hands-on production experience across GCP and AWS: built FastAPI microservices on Cloud Run and delivered AWS Lambda + ECS Fargate systems with Terraform/GitHub Actions. Strong in data engineering (Glue/Spark, S3/Redshift) and modernization (SAS to Python/SQL), with proven reliability and incident ownership—including cutting a 20+ minute reporting query to under 2 minutes.”

“Backend/data engineer with production experience building secure Django/DRF APIs (JWT RS256 + rotating refresh tokens), background processing with Celery, and strong reliability practices (timeouts, retries/backoff, structured logging, audit trails). Has delivered AWS solutions spanning Lambda + ECS with IaC/CI-CD and built Glue/PySpark ETL pipelines with schema evolution and data-quality quarantine patterns; also modernized a legacy SAS pipeline to Python/PySpark with parallel-run parity validation and phased rollout.”

“LLM/agentic systems builder who has deployed multi-agent educational chatbots using LangChain + LangGraph, with LangFuse-based tracing and FastAPI hosting. Focused on production reliability and performance (latency reduction via agent decomposition and caching) and on evaluation/testing (routing test scenarios, LLM-as-judge). Partnered with product to add image understanding by parsing and storing images in S3, expanding chatbot coverage to 30+ books with images.”

“Open-source contributor who improved React Query’s caching/subscription behavior to reduce unnecessary re-renders via debouncing and batched updates, validated with benchmarking and extensive tests. Also maintained a Flask extension and resolved production background-task hangs by tracing Redis connection handling issues, adding cleanup/retry logic and troubleshooting docs. In a fast-paced startup, owned the design of a Celery+Redis multi-queue background processing system with Prometheus-based observability.”

“Embodied AI / robotics-focused ML engineer with experience at JPMorgan and EY building language-to-robot control systems that connect transformer/LLM intent to safe real-world robotic actions. Designed production-grade, low-latency architectures (Kafka/Redis, monitoring, CI/CD) and applied sim-to-real and model distillation to make research ideas deployable on physical systems.”

“At HPE, led and deployed an enterprise-grade LLM document intelligence platform for an insurance client, automating extraction from highly variable PDFs/scans/emails and raising field accuracy from 74% to 93%. Built a LangChain/Pinecone/OpenSearch RAG framework to cut hallucinations by 37% and operationalized LangSmith evals in CI, driving a 41% triage accuracy lift and >33% fewer incorrect resolutions while partnering closely with claims operations via HITL workflows.”

“Fintech-focused engineer who built an end-to-end KYC verification pipeline for advisor onboarding using Flask microservices, Celery/Redis, and AWS (Lambda/ECS/EC2) with CloudWatch-driven scaling and latency optimizations. Also shipped a production internal knowledge assistant using RAG + embeddings/vector search with guardrails (similarity-based fallback, prompt-injection protections) and an evaluation loop with compliance specialist review that drove measurable retrieval improvements.”

“Backend/DevOps engineer pivoting into robotics/space, building hands-on ROS2 (Humble) skills via Gazebo simulations and experimenting with Nav2 and slam_toolbox. Brings strong distributed-systems and real-time debugging practices (profiling, instrumentation, QoS/retry patterns) and is actively learning perception and control fundamentals to transition into autonomous robotics.”

“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.”

“GenAI/ML engineer (CitiusTech) who has deployed production RAG systems for compliance/operations document Q&A, using Pinecone + FastAPI microservices on Kubernetes with strong monitoring and guardrails. Also built a GenAI-powered incident triage/routing solution in collaboration with non-technical stakeholders, achieving 35% faster response times and 40% fewer misclassified tickets, and has hands-on orchestration experience with Airflow and AutoSys.”