Senior Machine Learning Engineer, SAP

Job Description

SAP is hiring a senior machine learning engineer to lead the design and delivery of production-scale machine learning and generative AI systems. The role focuses on developing scalable AI solutions that can scale, model optimisation, MLOps capabilities, advanced LLM use cases, and enhanced performance. It also involves technical leadership, engineering mentoring, evaluation, governance, and responsible AI.

Qualification: Bachelor’s Degree/Master’s Degree in a relevant technical field

Experience: 7–9+ years

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Responsibilities

• Transform business requirements into AI solutions that can scale according to the demands of customers and products.
• Create data pipelines, feature stores, model training, model serving, and observability.
• Construct advanced models using deep learning, NLP, ranking, recommendation, forecasting, retrieval, and large language models.
• Optimize for selecting models, training them in a distributed fashion, inference, GPU utilization, compression, prompting, and retrieval.
• Develop reusable AI frameworks, services, and platform capabilities that support diverse enterprise use cases.

Requirements

• More than 7 years of experience in software engineering and machine learning for production AI systems.
• Expert knowledge of programming in Python and expert knowledge of software engineering with Java/Go.
• Expertise in fields ranging from machine learning, deep learning, optimisation, natural language processing, search, ranking, and recommendations.
• Familiarity with all the components of the end-to-end machine learning pipeline, including ingestion through to experimentation, deployment and maintenance of machine learning systems.
• Hands-on knowledge of frameworks such as PyTorch, TensorFlow, scikit-learn, embeddings, vector databases, RAG, and model serving.

Preferred Qualifications

• Experience with hands-on work with generative AI, including RAG pipelines, agents, prompts, and guardrails.
• Experience with hands-on work in MLOps, with understanding of model registry, feature store, CI/CD, IaC, and experiment tracking.
• Experience with hands-on work in distributed systems, event-driven architecture, stream processing, and cloud-native ML platforms.
• Experience with hands-on optimisation of AI applications with a focus on performance, scalability, reliability, latency, cost, and responsible AI.
• Experience with hands-on work in mentoring of engineers and scalable architecture.