Software Engineer (Data & AI)

Aufgaben

Objective of job

Contribute to the future of Mercedes-Benz in China by leading the charge in AI innovation - build innovative solutions, drive rapid prototypes, and scale AI to new heights. As part of the AI&Data Value team, the position is responsible to deliver data-driven and AI-powered solutions that help our organization to achieve efficiencies and that excite our customers.

• Responsible for developing and evaluating Generative AI, AI and Data use case applications through application of LLMs (and other generative models), statistical machine learning techniques and data analytics to address key business challenges within Mercedes-Benz China.
• Lead Rapid Prototyping and PoC Delivery: Lead the rapid development of Proof-of-Concept (PoC) AI applications to demonstrate value and feasibility.
• Develop and deploy AI solutions that are scalable, reliable, and maintainable in a production environment.
• Responsible for contributing to the development and maintenance of foundation infrastructure to enable future data, AI & GenAI use cases.
• Design and implement robust data pipelines and infrastructure to ensure high-quality data availability for AI model training and deployment.
• Responsible for contributing to and sharing best practice, know-how and participating in relevant technical discussion and governance forums in a subject matter expert capacity.
• Continuously research and evaluate new AI technologies and techniques to ensure Mercedes-Benz China remains at the forefront of AI innovation.

Job designation

• Design, develop and deploy, as well as evaluate Generative AI, AI and Data use case applications through application of LLMs (and other generative models), statistical machine learning techniques and data analytics.
• Lead the rapid development of Proof-of-Concept (PoC) AI applications to demonstrate value and feasibility:

- Take full ownership of the PoC development lifecycle, from initial concept to demonstration. This includes understanding the business problem, defining the scope of the PoC, and identifying key success metrics.

- Design and implement AI-powered prototypes quickly and efficiently, leveraging existing tools, frameworks, and cloud resources to accelerate development.

- Evaluate and select the most appropriate AI/ML algorithms, tools, and technologies for each PoC, considering factors such as performance, scalability, and cost.

- Train and evaluate AI/ML models using appropriate techniques, and iterate on model design to achieve desired performance.

- Work closely with business stakeholders, and other engineers to gather requirements and incorporate feedback into the PoC.

- Identify and mitigate potential risks associated with the PoC, such as data availability, model performance, and integration challenges.

- Work with applications teams to transition successful PoCs into production-ready AI applications, ensuring scalability, reliability, and maintainability.

• Identify and acquire relevant data sources for designed AI solutions and build and maintain robust data pipelines to ingest, process, and transform large datasets from various sources.
• Contributing to the development and maintenance of foundation infrastructure to enable future data, AI & GenAI use cases.
• Contributing to and sharing best practice, know-how and participating in relevant technical discussion and governance forums in a subject matter expert capacity. Stay up-to-date with the latest advancements in AI and related technologies.

Qualifikationen

Qualification

• Bachelor or Master’s in a quantitative degree (Computer Science, Engineering, Physics, Maths) or equivalent professional experience
• 6+ years experience in related technical field (software engineering, data engineering, data science)
• Strong proficiency in software development principles, data structures, algorithms, and design patterns.
• Strong Expertise in programming languages commonly used in AI/ML development (Python, Java, Scala, etc.)
• Familiarity with LLM application stack (e.g. Langchain, Dify, Huggingface, Pytorch) and techniques (e.g. RAG, Embeddings, Fine-tuning etc..)
• Familiarity with machine learning packages (sklearn, xgboost etc…)
• Solid theoretical fundamentals for statistical methods, machine learning and generative models
• Strong optimization modelling and statistical modelling experience and data aggregation skills, being an expert in analyzing large, complex, multi-dimensional data sets.
• Experience with cloud platforms (Azure, AWS and/or AliCloud) and deploying AI solutions in the cloud.
• Communication and interpersonal skills, with the ability to effectively communicate technical concepts to both technical and non-technical audiences.