Site Reliability Engineering Experts in Berlin

• 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

• Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
• Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
• Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
• Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
• Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow

IOG (IOHK), is one of the world's pre-eminent blockchain infrastructure research and engineering companies.

• Converted a lingering R&D project into a cohesive, production-ready testnet; built and scaled the 35-member engineering team (Core, QA, SRE) to achieve this goal.
• Defined strategic direction and aligned technology development with business objectives as a key member of the leadership.
• Optimized software development processes and implemented agile methodologies, enhancing operational efficiency and code security.
• Delivered projects in a fast-paced startup environment through effective project management and resource allocation.

• Building a cross-functional product team to develop a super app
• Advising and mentoring to support the team and provide input (technical & soft skills)

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.

• Consulting an NGO in the field of data center sustainability in publicly funded projects (BMUKN with NADIKI and Umweltbundesamt with SIEC)
• Development of Python APIs and web applications, deployment on AWS/ECS with Terraform
• Collecting power consumption metrics for servers, CPUs, GPUs running AI workloads
• Technologies used: AWS, EC2, ECS, Fargate, CloudMap, VPC, Route53, Lambda, EventBridge, CodeBuild/CodePipeline/CodeDeploy, Terraform, Docker, Linux, Bash scripting, Python, Flask, SQLAlchemy, SQL, MariaDB, InfluxDB, Telegraf, Prometheus, Zabbix, Kubernetes, Letsencrypt, certificate management

• Defined and executed a comprehensive security roadmap: integrated Shift-Left Security, CNAPP, and DevSecOps principles to streamline secure product development and reduce risk exposure.
• Established a robust threat modeling framework: embedded security into design processes, enabling early identification of vulnerabilities and reducing potential risks.
• Developed a scalable Vulnerability Management program: accelerated detection and remediation of new vulnerabilities, significantly shortening the risk response cycle.
• Enhanced cloud and container security: leveraged advanced tools such as Tetragon to achieve deeper visibility and implement a defense-in-depth strategy.
• Automated security controls within CI/CD pipelines: integrated security measures into the development lifecycle to maintain continuous delivery with robust safeguards.
• Championed cross-functional collaboration: partnered with developers and infrastructure teams to prioritize threats and align remediation efforts, fostering a unified security culture.
• Ensured regulatory compliance and audit readiness: collaborated closely with the InfoSec team to adhere to internal policies and successfully support audits for standards like PCI-DSS and SOC2.

• Leading 20+ staff in development, site reliability engineering, and architecture.
• Leading the group-wide agentic AI initiative (Norway, Poland, Germany).
• Hands-on solution architect and AI consultant for over 50% of my working time on client projects in the publishing sector – from local publishers to international corporations.
• Strategic consulting and technical implementation of AI workflow platforms (n8n, Workato).
• Developing prototypes for traditional, AI-based, and agentic AI workflows.

• Owning, designing, securing, maintaining reliability of the cloud based platform environments
• Increasing developer productivity and growing the infrastructure team horizontally and vertically to 5 engineers
• Realised infrastructure cost reduction of 50% by utilizing efficient spot instances
• Improved PE developer efficiency by 15% with survey led development process

• Designed a platform based on IoT, Azure, Kubernetes, and Postgres for an existing application
• Migrated from "click-ops" and UI-defined CI/CD pipelines to infrastructure-as-code with Terraform, enabling complete redeployment of multiple environments
• Technologies: Terraform, OpenTofu, Azure, Azure DevOps, Kafka, IoT, Kubernetes, Grafana, Prometheus, GitOps, relational databases