Job Description
* AI Solution Development: Design and implement end-to-end AI solutions on Azure. This includes developing machine learning models (e.g. predictive algorithms, classification, anomaly detection) and integrating them into cloud applications. You will write code (primarily in Python) to build and train models, leveraging Azure Machine Learning, Azure Databricks, or custom frameworks as needed.
* Build and deploy intelligent applications using Azure AI Foundry, leveraging prompt orchestration, grounding with Azure AI Search, and agent orchestration features for enterprise-scale GenAI use cases. Independently implement GenAI workflows using Foundry.
* Generative AI & LLMs: Play a key role in developing Generative AI solutions for clients. Experiment with and implement large language model (LLM) based applications using Azure OpenAI Service and frameworks like LangChain for building conversational agents or Retrieval-Augmented Generation (RAG) pipelines. For example, you might build a chatbot that uses an LLM with enterprise data or create a summarization service for insurance documents. Ensure prompt engineering and fine-tuning are done in a robust, secure manner.
* Azure AI Services Integration: Utilize Azure Cognitive Services (e.g. for vision, speech, language) and Azure AI services to speed up solution development. For instance, integrate Azure Cognitive Search for semantic search in a knowledge retrieval solution, or use Form Recognizer in a document processing pipeline. Combine these services with custom ML where appropriate to meet client requirements.
* MLOps and Deployment: Take ownership of deploying and operationalizing ML models. You will containerize models or use Azure ML endpoints, set up CI/CD pipelines (using Azure DevOps or GitHub Actions) for automated model training and deployment, and implement monitoring for model performance drift. Ensure that the AI solutions can scale in production and adhere to DevOps best practices for reliability.
* Data Pipeline Collaboration: Work closely with Data Engineers to ensure the data needed for AI models is available, reliable, and well-prepared. Contribute to data pipeline design for ML – e.g., help define feature engineering processes or streaming data ingestion for real-time inference – so that models can be trained on and serve high-quality data.
* Cross-Functional Collaboration: Collaborate with Solution Architects (like the ML/AI Architect) to translate high-level architectural designs into concrete implementation tasks. Work alongside other specialists such as data scientists and cloud engineers to build secure, end-to-end AI solutions that integrate into the client’s ecosystem. This includes participating in code reviews, knowledge sharing, and troubleshooting sessions within the team.
* Client Support & Iteration: Support client teams in adopting and understanding the AI solutions. This could involve preparing technical documentation, demoing functionalities to stakeholders, and iterating on models based on user feedback or changing requirements. Ensure that AI solutions meet responsible AI guidelines and data privacy/security standards as expected by enterprise clients.
Qualifications
* Experience: 5+ years of hands-on experience in AI/ML engineering or data science roles, with a focus on building and deploying solutions (not just research or academic projects). You should have taken multiple ML projects through the full lifecycle from data preparation to production deployment.
* Language Skills: fluent in german and english
* Azure & Data Skills: Strong experience with the Azure AI/Data ecosystem. This includes proficiency with Azure Machine Learning (for running experiments, managing models), familiarity with Azure Databricks or Spark for big data processing, and usage of Azure data services like Data Factory, Azure Data Lake, or Synapse Analytics. Experience integrating Azure Cognitive Services or Azure OpenAI into applications is a big plus.
* Experience using Azure AI Foundry for building LLM-based applications or orchestrating AI workflows. Understanding of Foundry templates, grounding data sources, and integration with Azure OpenAI and Azure Search is expected.
* ML & Programming: Proficiency in Python and common ML/DL libraries (pandas, scikit-learn, TensorFlow or PyTorch, etc.). Ability to write clean, efficient code for data processing and model training. Experience with NLP or computer vision techniques is helpful. You should also be comfortable with Git and collaborative coding practices.
* MLOps Knowledge: Practical understanding of MLOps and model lifecycle management. Experience setting up automated training pipelines, model versioning, CI/CD for ML, and using tools like MLflow for tracking experiments. Knowledge of Docker and Kubernetes (e.g., Azure Kubernetes Service) for deploying ML services is advantageous.
* Generative AI & LLMs: Exposure to Large Language Models or other generative AI technologies. You should understand how to use pre-trained models (like GPT-based models) and fine-tune or prompt them for specific tasks. Direct experience with libraries/frameworks for LLMs (LangChain, Hugging Face Transformers, etc.) or vector databases for RAG is a strong plus, as many of our projects explore these emerging areas.
* Problem-Solving & Communication: Strong problem-solving skills with the ability to debug complex issues in data pipelines or model performance. Equally important are good communication skills – you can explain technical work to team members and occasionally to client technical staff. You work well in a team and can mentor more junior colleagues if they join (even though we currently hire only experienced professionals).
* Certifications: Azure and data/AI certifications are highly regarded. Ideally, you hold or are willing to obtain Microsoft Certified: Azure AI Engineer Associate (AI-102) certification, which demonstrates ability to design and implement Azure AI solutions. Other relevant certs like Azure Data Scientist (DP-100) or Databricks Generative AI Engineer certification are a bonus. Devoteam will support you with time and resources to achieve certifications.
* Education: Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent work experience). A master’s in AI/Data Science is a plus but not required.
Additional Information
You will be part of a collaborative, remote-friendly team that values continuous learning and
delivering impact through modern cloud-native data solutions.