What you`ll do
The Role
In this role, you will drive internal AI adoption across different Lines of Business (LOBs) within the company. You will collaborate with other engineers to develop AI services, taking projects from proof of concept to full production deployment. This position involves close partnership with AI Platform engineers and stakeholders from various departments. You will work with cross-functional teams to contribute to our evolving AI-first tech stack, address diverse and rapidly changing challenges, and thrive in a fast-paced environment that offers continuous learning. Additionally, you will help define and promote best practices for AI engineering excellence throughout the organization.
Responsibilities
Lead and develop AI services from PoC to Production
Setup and develop new Big Data pipelines, to productize data ETL workloads for different AI services.
Develop GenAI/AgenticAI solution designs
Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
Productionize AI PoCs with good software engineering by incorporating CI/CD pipelines, deployment and MLOps/LLMOps.
Take ownership over AI services in development and production
Guide and mentor junior colleagues and evolve their AI engineering skillset
What you bring
Bachelor's or master's degree in machine learning, computer science, engineering, or related technical fields
Vast coding knowledge in Python
Knowledge of Distributed Computing Systems for Big Data Engineering such as Databricks (PySpark/Scala) is beneficial
Exceptional understanding and coding experience of genAI/ agenticAI frameworks such as crewAI / langgraph etc.
Proven hands-on expertise in code-based LLM consumption from platforms such as OpenAI or Huggingface
Track record of using Python backend development frameworks such as e.g. Flask, Django, Quart or FastAPI
Vast general software engineering best-practices (e.g. Object-Oriented Programming) and experience working in a scrum-like software engineering unit.
Productization technologies and tools such as Docker, K8s, GitHub, VSCode, Linux etc.
CI/CD and ML Ops frameworks such as Jenkins, GitHub Actions, MLFlow, etc.
Cloud-deployment offerings such as Azure ML, Databricks, AKS, Event Hubs, Azure Data Factory, Azure Functions or similar in AWS / GCP
Some form of code ownership and scrum team leadership experience
Curiosity to learn about different AI applications and new technologies in general
Excellent spoken and written knowledge of English as well as collaborative mindset
Experience with SAP AI offerings e.g. AI Core, AI Business Services, Joule etc. is a plus
Understanding of Agile / Scaled Agile (SAFe) methodologies is a plus
We win with inclusion
Successful candidates might be required to undergo a background verification with an external vendor.
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