18.09.2026 aktualisiert

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Machine Learning Engineer / Data Scientist / Cloud Architect / Test Automation Engineer

Luzern, Schweiz
Luzern +100 km
PhD in Physics (University of Toronto)
Luzern, Schweiz
Luzern +100 km
PhD in Physics (University of Toronto)

Profilanlagen

CV-Wolfram-Helwig-DE.pdf
CV-Wolfram-Helwig-EN.pdf

Über mich

A Swiss Army knife — now actually based in Switzerland. Coding since childhood, physicist (PhD), came to algorithms through game theory and from there to cloud and machine learning. Curious by nature, quick to find my way around new domains. Lately: production systems built with and for AI agents.

Skills

Künstliche IntelligenzApache AirflowMachine LearningTensorFlowAzure Machine LearningSqlalchemyAWS CDKTest-Driven DevelopmentData ScienceAzure Data FactoryPyTorchLarge Language ModelsDeep LearningCloud TechnologiesGitLabPytestData LakeScikit-learnTerraformJenkinsDatabricks
Freelance software engineer specialising in Python, machine learning and cloud architecture (Azure, AWS). Physicist (PhD) with more than 10 years of experience in software development, data science and test automation.
Currently: backend architecture and test automation in a large-scale insurance programme. Based in Lucerne, Switzerland — available from September at reduced capacity and at full capacity from November, remote-first with on-site presence in the Lucerne / Zug / Zurich area by arrangement.
Core competencies:
  • Languages & frameworks: Python (PyTorch, TensorFlow, JAX, scikit-learn, pandas, NumPy, FastAPI, Django, Flask, SQLAlchemy, pydantic), Jupyter, TypeScript / JavaScript, React, Java, C++, SQL (PostgreSQL, DB2), GraphQL
  • Machine learning & AI: Deep learning (CNN, RNN, Transformers, attention), NLP & LLMs, generative models (diffusion, LoRA fine-tuning), reinforcement learning (DQN, PPO, A2C, MCTS, CFR, NFSP), classical ML (regression, random forests, gradient boosting, clustering)
  • AI agents: Orchestration of coding agents (Claude Code, Codex), agent-memory architecture, skill design, tool integration over MCP (Model Context Protocol), enforcement-gate design
  • Cloud & data engineering: Azure (Machine Learning, Functions, Data Lake Storage, AKS, Databricks), AWS (Lambda, S3, DynamoDB, Kinesis, SQS/SNS, Step Functions, SageMaker, API Gateway), Docker, Kubernetes, Apache Spark / PySpark, Terraform, AWS CDK, Serverless Framework
  • Quality & testing: Test automation in large-scale programmes (Robot Framework, pytest), test-driven development, automated test and deployment pipelines, test-quality reporting, business process analysis (Camunda)
  • Ways of working: CI/CD (Jenkins, GitLab, Bitbucket), agile / Scrum, monitoring (CloudWatch, Sentry, Elastic / Kibana), infrastructure as code
Certifications:
  • Microsoft Certified: Azure Data Scientist Associate
  • Microsoft Certified: Azure Data Engineer Associate
  • Microsoft Certified: Azure AI Engineer Associate
  • Microsoft Certified: Azure Fundamentals
  • EXIN Agile Scrum Foundation

Erfahrung

Berufserfahrungmehr als 10 Jahre
Freelancer seit2021

Sprachen

DeutschMutterspracheEnglischverhandlungssicher

Projekthistorie

AI Engineer

Own project: mnemo — memory engine for AI agents
  • Memory system for AI coding agents: tiered short-, mid- and long-term memory across agent sessions
  • Autonomous nightly consolidation runs with auditable reports; enforcement gates for completion, blocked-state and confidentiality checks
  • Built with AI coding agents: skill design and orchestration across two agent CLIs and multiple working copies — github.com/StochasticEntropy/mnemo (MIT)
Technologies used: Claude Code, Codex, agent skills, Git, launchd, shell/zsh, Python

Full-Stack Engineer

Own project: runcomposer — test execution platform
  • Designed and built an API-first platform for orchestrating large automated test suites: selection driven by a tag taxonomy and rule tree, with a compile step that previews exactly which cases a filter matches before a run starts
  • Run-configuration generation and handoff to CI, re-run filters for previously failed tests, strict versioned selection contract
  • FastAPI backend with pydantic contracts; React/TypeScript front end for filter trees, match preview and run history
  • Built with AI coding agents; in use in a live test-automation mandate and released under the MIT licence — github.com/StochasticEntropy/runcomposer
Technologies used: Python, FastAPI, pydantic, React, TypeScript, Robot Framework, Jenkins, YAML, pytest, Claude Code, Codex, Git

Senior Software Engineer

Insurance
  • Designed a green-field software architecture in Python for a large-scale test automation project
  • Developed the Python keyword libraries and the business test logic behind the Robot Framework suites
  • Implemented the backend data access layer (SQLAlchemy, DB2) and the REST/SOAP integration of further components
  • Built and operated the pipelines for automated tests and deployments (Jenkins, Ansible)
  • Developed an internal reporting tool (FastAPI) for the daily regression overview — failure patterns, annotations and history for the team
  • Analysed business processes in Camunda as the basis for test cases
  • Debugged the Java application layer (IntelliJ)
  • Agile collaboration in a distributed team (Scrum, Jira, Confluence, Bitbucket)
Technologies used: Python, Robot Framework, FastAPI, SQLAlchemy, SQL, DB2, Jenkins, Ansible, REST, SOAP, pydantic, Java, Camunda, Bitbucket, Git, Jira, Confluence

AI Engineer

Own project: local studio for generative narrated videos
  • Fully local, agent-orchestrated video studio on Apple Silicon — image synthesis, narration and cutting entirely on the machine
  • Generative layer: identity fine-tuning (LoRA) on diffusion models (SDXL, FLUX), image-to-video synthesis (Wan 2.2), frame interpolation and upscaling; composition as code with Remotion, clocked by the locally synthesised narration track
  • My part: the vision, the pipeline design and the acceptance criteria — objective take selection via word error rate and pitch distance, identity fidelity via face embeddings, blind LLM judge panels; the AI coding agents built it, deliberately as a test of how far that carries
Technologies used: Claude Code, Codex, Remotion (React/TypeScript), MLX/Metal, SDXL, FLUX, LoRA, Wan 2.2, Qwen3-TTS, Whisper, InsightFace, RIFE, Real-ESRGAN, ffmpeg, Python, Jupyter, Apple Silicon

Software Engineer

Own project: robot-companion — VS Code extension for Robot Framework
  • Editor tooling that surfaces return values, enums and types for Robot Framework: two synchronised sidebar panels for documentation preview and a return explorer
  • Hover information for local variables, named arguments and keyword return structures; autocomplete for type-matched variables and return members, fed from the indexed Robot and Python documentation
  • Deliberately read-only — no formatters, no diagnostics — so the extension runs alongside existing Robot Framework tooling instead of competing with it
  • Built with AI coding agents; published on the VS Code Marketplace under the MIT licence — github.com/StochasticEntropy/robot-companion
Technologies used: JavaScript, Node, VS Code Extension API, Robot Framework, Python, Claude Code, Git

Senior Backend Engineer

Smart mobility
  • Implementing AWS Lambda Serverless code to process incoming Kinesis streaming data and Frontend requests.
  • Frontend development with JavaScript and React
  • Containerized deployment of backend code with Docker and FastAPI
  • Bug tracking with Elastic, Kibana and CloudWatch
  • IaC with Terraform and Serverless Framework
Technologies used: AWS Lambda, API-Gateway, SQS, SNS, Kinesis, Terragrunt/Terraform, Serverless, Elastic, CloudWatch, Python, Git, PostgreSQL, Hasura, GraphQL, REST, Docker, GitLab

Python Cloud Developer

E-commerce
  • Developed a serverless, API-driven data management system on AWS (Lambda, DynamoDB, SNS, SQS, API Gateway)
  • Implemented the AWS Lambda layer between customer API requests and the backend NoSQL DynamoDB and S3 data stores
  • Developed a BERT-based NLP model and integrated it into the serverless architecture
  • Maintained the OpenAPI specifications; monitoring via CloudWatch and Sentry; IaC with AWS CDK
Technologies used: AWS Lambda, DynamoDB, S3, Step Functions, API Gateway, SQS, SNS, AWS CDK, Terraform/Terragrunt, CloudWatch, Sentry, Python, Hugging Face, Rockset, SQL, Bitbucket, Git

Coach for Machine Learning / Deep Learning

Automotive
  • Delivered an interactive course to a group of 20 participants
  • Presented classical machine learning tools and concepts based on Python, numpy, pandas and scikit-learn
  • Presented deep learning tools and concepts based on TensorFlow v2, including CNNs, RNNs and Transformers
Technologies used: Python, TensorFlow v2, Keras, scikit-learn, numpy, pandas, Jupyter Notebooks, Azure VMs

AI & Cloud Engineering

Own projects: game theory & deep reinforcement learning
  • Developed and applied deep-learning methods: computer vision (CNNs, vision transformers), NLP (transformer-based models including BERT)
  • Deep reinforcement learning for imperfect-information scenarios (DQN, REINFORCE, A2C, PPO) and game-theoretic methods (MCTS, CFR, NFSP)
  • Designed and backtested quantitative trading algorithms with systematic result analysis
  • Implemented and reproduced recent research results in deep and reinforcement learning
  • Data science and distributed machine learning on Microsoft Azure and Databricks — the groundwork for the subsequent Azure certifications
Technologies used: Python, TensorFlow, Keras, PyTorch, JAX, Hugging Face, scikit-learn, NumPy, pandas, Jupyter, Docker, Apache Spark, Azure ML, Databricks, Data Lake Storage, GCP, TPUs

Algorithm Developer

  • Developing algorithms for the simulation of milling processes based on 3D-CAD data
  • Geometric complexity analysis of the milling process based on volume and surface properties
  • Implementation of the developed methods (Java)
  • Test-driven development (JUnit)

IT Architecture Consultant

  • Consulting in the exploration phase of a new Big Data System
  • Collection of business and technical requirements for the new system
  • Derived and presented suitable IT-architectures based on the collected business and technical feedback

Algorithm Developer

  • Mathematical optimization of algorithms for distance field calculations in a 3d analysis software
  • Implementation of the developed methods (Java) from prototyping to release.
  • Test-driven development (JUnit)

IT Consultant

  • Quality assurance of in-car control unit tests
  • Developed tools (Java, Python) for automatic test quality reporting

IT Consultant

  • Design and specification of a customer-specific software application to be in accordance with WLTP standards
  • Development of the required data structure
  • working in an agile environment

Quantitative Strategies

Own project: game theory
  • Built abstractions for computationally unsolvable strategic scenarios under imperfect information
  • Game-theoretical analysis: derived optimal strategies (Nash equilibria) over those abstractions
  • Generalised the resulting insights back to the full problem
  • Applied machine-learning methods (decision trees, clustering) to identify strong strategies in collected data
  • Successfully applied the developed strategies under real-time conditions
Technologies used: C++, Python, scikit-learn, Jupyter, PostgreSQL, Visual Studio

Research / Teaching Assistant

  • Analyzed transformation by local operations of multi-party distributed entangled quantum states in high dimensional Hilbert spaces
  • Theoretical study of the utilization of maximally entangled quantum states in cryptographic protocols and error correction algorithms
  • Proved conditions for the existence of maximally entangled state in a high dimensional multi-party settings
  • Computational investigation (Java) of subspaces of Hilbert spaces to find absolutely maximally entangled states
  • Lead tutorials for undergraduate students

Research / Teaching Assistant

  • Numerical and theoretical evaluation of multi-mode effects on the security of quantum cryptographic protocols
  • Wrote simulations in C++ to evaluate the security of quantum cryptographic protocols
  • Lead tutorials for courses in theoretical physics

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