AI Engineers 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 deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
• Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
• Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
• Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
• Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
• Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
• Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.

AI Platform Backend – Senior Developer

Brought in to design and build a backend for an AI platform from scratch, including multi-provider LLM orchestration and real-time infrastructure for AI influencer personas at scale.

Tasks and responsibilities

• Architected and implemented a multi-LLM orchestration layer with Semantic Kernel to integrate GPT-4 and other providers for core platform logic and AI influencer personas, reducing model-switching overhead by abstracting provider APIs behind a single interface.
• Designed and developed a backend from scratch in C# / .NET 10, including domain modeling with DDD, a versioned RESTful API layer, and cloud infrastructure setup on Azure.
• Built a real-time chat infrastructure with Server-Sent Events (SSE), message persistence, and delivery guarantees for live operation of AI influencer personas at scale.
• Developed a media management service with integration of cloud object storage for upload and retrieval of influencer-generated content.
• Created an integration and unit test suite with data seeding for reliable regression testing across all core platform flows, significantly reducing the production error rate.

Tools and technologies: C#, .NET, ASP.NET Core, Python, TypeScript, MySQL, Semantic Kernel, EF Core, Minimal APIs, LLM Orchestration, Prompt Engineering, Agentic AI, Generative AI, AI-Assisted Engineering, Claude Code, GitHub Copilot, Google Gemini, OpenAI API, Ollama, Redis, Azure, Azure Container Apps, Azure Database for MySQL, Docker, GitHub Actions, Clean Architecture, Vertical Slice Architecture, CQRS, Domain-Driven Design, REST API, xUnit, Integration Testing, Unit Testing, Jira, Confluence, Scrum

• Converts natural language into JQL using RAG and pgvector. Returns structured JSON with a query, chart spec, and plain-text answer. A two-stage router answers general questions without touching the JQL pipeline at all.
• Interchangeable LLM backends: Ollama, vLLM, Groq, Anthropic Claude, AWS Bedrock - switchable at runtime, no code changes. Self-healing JQL: on Jira validation failure, feeds error back to LLM, retries up to 4 times. OCI Vault for secrets. Deployed on Oracle Cloud A1 with GPU inference over Tailscale private network. Open source.

• Fractional leadership; consulting growth-stage startups and scale-ups on data strategy, ML products, and platform foundations
• Building decisioning systems for growth, personalization, & product experimentation, across e-Commerce, Digital Health, Energy, and Logistics
• Exploring Agentic AI & LLM-based tooling for production readiness patterns

Enterprise AI content categorization and AI-powered web research.

• Built multi-LLM evaluation framework with annotated data
• Iterated LLM error rates based on annotated datasets
• Implemented AI-powered web research pipeline Stack: LLM, evals, OpenRouter, Python, Node.js, TypeScript, React

• 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

Conceptualizing and developing AI-powered app MVPs from the first idea to a working product. I combine UX strategy with modern AI and no-code tools — especially Claude and Claude Code, where I work directly in the terminal on real code (React, Vite), complemented by Figma, Bolt.new, and Base44 — to quickly validate and build product ideas. Personal platforms: Nutrycoach.ai (AI nutrition coaching with role-based dashboards for clients, coaches, and admins, AI meal analysis, and a scalable design system) and LuxuryBandit (content/creator app). This led to my own practice 'AI-assisted App Strategy', which combines human creativity, UX logic, and intelligent automation.

• Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
• Collaborated on product features, achieving measurable impact and maintaining strong client relationships.

• Codebase Evaluation & Problem Design: Designed and stress-tested complex software engineering problems against large open-source Python codebases (including pandas), requiring deep context acquisition and architectural understanding to produce well-scoped, realistic problem statements aligned to strict correctness guidelines.
• Agent Failure Analysis: Assessed LLM coding agent solutions for correctness and completeness, identifying meaningful failures across edge case handling, dtype behaviour, and multi-column NaN propagation logic; documented findings with precision for downstream evaluation use.
• Programmatic Test Suite Development: Authored comprehensive pytest suites to programmatically verify agent-generated solutions against defined requirements, with deliberate coverage of boundary conditions and failure modes not caught by naive implementations.
• Containerised Environment Engineering: Built and debugged Docker environments for reproducible agent execution, including git-based repository provisioning, dependency pinning with npm ci, and multi-stage Dockerfile authoring across Linux-based containers.

• Introduction to AI and Automation
• The Automation Project
• Make.com Foundations
• Automation Basics: Data Management
• Document Workflows, Troubleshoot, and Automate Reports
• Digression: Data as the Foundation of AI Systems
• Implementing AI Solutions in Practice
• Digression: Large Language Models (LLMs)

• Analysis of complex business processes and end-to-end user journeys in digital product and platform environments
• Gathering, structuring, and prioritizing business and technical requirements (Functional / Non-Functional Requirements)
• Translating business goals into actionable requirements, user stories, and acceptance criteria
• Conducting stakeholder interviews, workshops, and reviews with business teams, IT, UX, and management
• Creating and maintaining requirement artifacts (BRD, FRD, user stories, process models, decision papers)
• Ensuring consistency between business needs, technical implementation, and product vision
• Close collaboration with development teams to clarify business questions during implementation
• Support with impact analyses (A/B tests), change requests, and scope management
• Quality assurance of implemented requirements including acceptance criteria and business testing
• Advising on the further development of product strategy and roadmap structure
• Prioritizing backlog items based on business value
• Defining and sharpening product goals, KPIs, MVP definition, and other success metrics
• Evaluating new features, tools, and initiatives from a user and business perspective
• Facilitating decision-making between business, product, and technology
• Supporting go-to-market considerations and product positioning
• Sparring partner for product and stakeholder decisions at management level
• Dashboard creation, data modeling, BI report administration, and data analysis in Power BI

• Providing freelance software engineering and AI orchestration services for an early-stage startup.
• Designing and coordinating autonomous AI systems capable of executing complex, multi- step workflows.
• Developing customer-facing pilots and proof-of-concept solutions.
• Participating in meetings with customers and investors to support product development and business discussions.

• Situation: Inherited a patchwork of aging proprietary systems accumulated over 20+ years — custom-built shop, pharmacy operations, logistics, and product data management. No standardization, no automation, no modern delivery practices.
• Team: Found a centralistic hero culture with one manager handling more than 20 direct reports. Removed the bottleneck, replaced low performers, brought resistant team members back on track or managed them out. Hired fresh talent that brought new energy and capability.
• Delivery: Unified nine brands into a single platform, improving delivery speed by roughly 10x and eliminating cross-brand inconsistencies. Launched a new mobile app that tripled mobile revenue share within 12 months.
• AI: Implemented AI coding tools across development departments. Continuously working on trainings and knowledge sharing for developers. Delivered analytics and agentic automation for customer care inbound emails, and initiated agentic call automation.
• Operations: Introduced automated build and deployment pipelines, initiated cloud migration, and brought the core pharmacy system onto a maintainable, current foundation.
• Governance: Took back project prioritization from ad-hoc business demands by introducing transparent capacity planning — the biggest “no” enforced and the most impactful change for the organization.