Helm Experts in Berlin

• Led migration to GCP using Terraform, GKE, and GitOps, improving deployment consistency and scalability
• Implemented Datadog observability stack via Terraform and datadog-operator
• Established automated end-to-end tests and on-call processes, improving incident response and service reliability
• Migrated from NGINX Ingress Controller to Kubernetes Gateway API (NGINX Gateway Fabric)
• Migrated stateful services (PostgreSQL and Redis) to GCP, improving scalability and operational reliability

Transmission system operator – network provisioning platform (team lead)

• Designed the saga orchestrator in Python that provisions network fabrics with rollback.
• Split IP, identity, and resource allocation into bounded contexts with their own databases and typed clients.
• Implemented data access using DDD repositories and units of work, with no SQL in the domain code.
• Required integration tests using real PostgreSQL and RabbitMQ containers, and API fuzzing for every service.
• Introduced an agent workflow where a spec starts each change and a human approves every merge.
• Rolled out OpenTelemetry auto-instrumentation through GitOps across eighteen Kubernetes clusters.
• Led incident response for stuck provisioning and recorded every outage as a post-mortem.

Languages and software used: Python, FastAPI, Pydantic v2, SQLAlchemy async, PostgreSQL, RabbitMQ, Celery, pytest, testcontainers, schemathesis, Helm, Flux CD, GitLab CI, OpenTelemetry, pi coding agent.

Software product company – datacenter simulation SaaS (backend development)

• Built the asynchronous simulation pipeline as the second-largest contributor among more than twenty team members.
• Tagged every image by commit SHA and semver in the GitHub Actions deployment workflows.
• Diagnosed the RabbitMQ memory alarm that froze every simulation and fixed the limits in the Helm chart.
• Converted the test suite to function-style pytest, with coverage reported in every pipeline.

Languages and software used: Python, FastAPI, SQLAlchemy, Alembic, Celery, RabbitMQ, Redis, PostgreSQL, S3, EKS, Helm, Keycloak, GitHub Actions, pytest, mypy, ruff, SonarQube.

Internal AI platform – model serving and telemetry

• Built the FastAPI service that deploys open-weight models on GPU tiers as OpenAI-compatible endpoints.
• Upgraded production EKS by two Kubernetes minor versions and fixed zone-bound storage faults.
• Traced every model call, including its token count, cost, and latency, through the OpenTelemetry operator.

Languages and software used: Python, FastAPI, vLLM, LangGraph, pydantic-ai, OpenAI and Anthropic APIs, EKS, Helm, Flux CD, Terraform, GitLab CI, OpenTelemetry, Grafana.

• Designed, built, and launched an internal event planning web application used by over 100 department leads for a large event, despite having no dedicated testing phase.

• Ensured smooth, failure-free operation during first production use, leading to the tool being adopted for future events.

• Automated catering calculations and related workflows, significantly reducing email communication and manual computation effort for meal planning.

• Managed deployment and hosting on a Linux server using Coolify, including application setup and runtime operations.

• Hired and guided a communication designer on UX while independently owning all technical decisions and implementation.

• Creation, maintenance and hardening of Kubernetes clusters employing Ansible and ArgoCD
• Keywords: Ansible, AWX, Kubernetes, NetApp, Prometheus, CI/CD ArgoCD, SSO, Fluent-bit, HAProxy, Calico, Keycloak, oauth2-proxy, SealedSecrets, kubeseal, Aqua kube-bench, CIS-Benchmarks, Aqua Trivy operator

• Developed and deployed microservices in Kotlin and Spring Boot, integrated AWS Secrets Manager to secure credentials and decreased network calls using Spring cache.
• Refactored Kafka consumer using Spring Kafka with semaphore-based backpressure to cap records and keep heap memory stable under spikes; switched to batch upserts to cut down on database invocations; added Testcontainers integration tests for Kafka and database to pave the way for future changes.
• Automated the financial reconciliation workflow in Spring Boot (Kotlin) using Spring Scheduler, transactional boundaries, JPA/Hibernate on MySQL, and Flyway migrations, saving the accounts team 16+ hours per week.
• Designed and dockerized payments end-to-end test framework in Robot (Python) with reusable keyword libraries and profiles; integrated with GitLab CI (JaCoCo XML and HTML reports) to accelerate releases and lift code coverage to 80%.
• Implemented end-to-end observability on Datadog by instrumenting services with Datadog APM, correlating metrics and logs, provisioning dashboards, and creating monitors with burn-rate alerts and anomalies to harden reliability and give stakeholders clear visibility.

• Drive migration of enterprise authorization platform from Styra DAS to open-source OPA via Skipper (Zalando's Golang-based ingress proxy) integration
• Optimise k8s resources and integrate native Prometheus metrics with OPA
• Migrate from internal monitoring solution to Prometheus CRs + Dash0

Tech Stack: Java/Kotlin, Golang, Python, Spring Boot, AWS, Kubernetes, Docker, OpenTofu, Prometheus, Grafana

OpsWorker turns Kubernetes alerts into root-cause analyses, on top of the monitoring a team already runs. I lead the technical side: the agent architecture, the AWS infrastructure it runs on (fully inside EU regions), and the engineering decisions behind it, read-only in the cluster by default, human in the loop for judgment. The stack underneath: Amazon Bedrock and Bedrock AgentCore, agents built with the Strands Agents SDK, the Claude and OpenAI APIs, and the Kubernetes API.

• Provide the Price Listing Service team with prices for all cars and vans in different markets
• Adapt market-specific requirements such as taxes, government subsidies, campaigns
• Import and synchronize new prices for all new and existing cars and store them in the Redis datastore
• Support our product and on-call duty
• Daily business, development of new features, bug fixing
• Pair programming, code review, mob programming
• Develop POCs for new ideas
• Maintain and extend the backend
• DevOps tasks
• Run Kubernetes updates
• Adjust and further develop Kubernetes resources
• Develop and adapt Helm charts
• Adapt and update ArgoCD
• Maintain, adapt, and improve CI/CD

• Own full-stack development of an AI-native enterprise platform built on TypeScript, React, Vite, tRPC, Hono, and PostgreSQL, delivering AI-powered consulting workflows to B2B clients.
• Designed and shipped a multi-agent AI system using ReAct framework and Claude skills-style workflow patterns, including an intelligent PM assistant with rich system prompts, slash commands, tool integrations, and streaming chat UI.
• Architected an LLM evaluation framework: rubric-based LLM-as-judge, golden datasets, regression testing, and automated quality gating — ensuring consistent AI output quality at scale.
• Integrated LangFuse for end-to-end LLM tracing, conversation replays, and evaluation pipelines, enabling data-driven prompt optimisation that reduced token costs and response variance.
• Built with Drizzle ORM, pgvector, and knowledge graphs for structured data access, semantic search, and relationship-aware AI reasoning across the platform.
• Led TanStack React Query migration across the application — replacing manual state management with centralised caching and automatic refetching, reducing data-fetching boilerplate significantly.
• Practiced AI-native development throughout: Claude Code, Codex, Perplexity SDK, and LLM-assisted testing across the full development lifecycle. Deployed on Vercel + Azure ACA with Biome for linting/formatting.

• Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
• Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
• Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
• Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
• Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
• Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
• Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
• Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
• Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
• Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices

• Developed and implemented organization-wide discovery framework based on Ulwick’s Outcome-Driven Innovation; enabled 7 Product Owners to systematically identify and quantify unrealized value through shared outcome language and opportunity scoring methodology
• Transformed Product Owner role from backlog clerks to strategic experimenters; established dedicated time budget for autonomous hypothesis testing and discovery activities
• Rebuilt customer journey maps to start at actual user need (tool selection phase) instead of platform entry point; eliminated manual data aggregation work previously done by project teams
• Implemented OKR framework across 4 product teams; defined quarterly objectives with measurable key results (e.g., 40% reduction in manual integration effort, self-service adoption increase)
• Unified 3 separate platform roadmaps through cross-team dependency mapping and shared service agreements
• Supported enterprise sales cycle with ROI modeling and technical due diligence for automotive and defense customers

• Built core parts of formae's infrastructure engine and cloud integrations in Go, managing resource lifecycles across cloud and Kubernetes environments.
• Designed and implemented an extensible plugin architecture and SDK for integrating infrastructure providers and resources, including AWS, Kubernetes and Helm.
• Built agent-native infrastructure workflows exposing formae through MCP to tools including Claude Code, Codex and Cursor.
• Worked directly with prospective customers on technical evaluations, implementation design and migration paths from existing infrastructure workflows to formae.
• Helped evolve formae through its open-source, multi-cloud and cloud platform releases, with a focus on reliable infrastructure orchestration and developer experience.

• Bootstrapping a CloudOps team and building a multi-cloud provider backend for a low-code Internal Developer Platform (IDP) with env zero

• Introducing user story mapping, ADRs, milestones, and backlog management

• Designing and developing core APIs, setting up CI/CD pipelines, OpenTofu/Terraform scripts

• Representing and communicating the team with third-party stakeholders (e.g. env zero)

• (Cross-)team coaching on DevOps, software design, Terraform, Golang, and agile practices