Principal Machine Learning Engineer

This role is fundamentally focused on engineering. We seek individuals who are builders capable of writing production-grade code, designing distributed systems, and resolving complex infrastructure challenges, rather than solely researchers developing isolated models. The successful candidate will treat machine learning models as integrated components within a highly scalable software architecture designed to withstand the demands of industrial operations.

How you'll demonstrate

Responsibilities:

• Study, transform, and deploy data science prototypes into production environments, ensuring they are scalable, efficient, and maintainable.
• Architect and write high-quality, scalable, and testable production code (Python, Scala, C++, or Java).
• Build robust APIs, distributed big data pipelines, and orchestration layers required to integrate AI into existing industrial master data systems.
• Navigate complex deployment environments.
• You will optimise inference bottlenecks, manage containerised deployments, and ensure our systems can handle massive volumes of unstructured industrial data reliably.
• Research and implement advanced ML algorithms (e. g., in NLP, Computer Vision) and extend existing ML libraries and frameworks.
• Run tests, perform statistical analysis, and monitor deployed models for performance, drift, and accuracy, implementing retraining strategies as needed.
• Work closely with data scientists, software engineers, and product managers to align ML solutions with business goals.
• Provide technical mentorship to junior team members, guide code reviews, and help define technical standards for the team.

Requirements:

• Bachelor's or Master's degree in Computer Science, Data Science, or a related field (PhD is often preferred for higher-level roles).
• 10+ years of industry experience in machine learning and software development.
• Strong programming skills in Python, C++, or Java.
• Expertise in frameworks like PyTorch, TensorFlow, Keras, or Scikit-learn.
• Familiarity with MLOps best practices and cloud platforms such as AWS, Azure, or GCP.
• Solid understanding of data structures, algorithms, and software architecture.

Preferred Qualifications:

• Deep expertise designing complex AI systems from the ground up, including fine-tuned multimodal models for dense diagrams (P& IDs, CAD), Agentic AI for multi-step reasoning, and advanced Graph RAG/Knowledge Graphs.
• Experience architecting modern lakehouses (e. g., Delta Lake, Apache Iceberg) to process and manage massive, complex datasets specific to manufacturing, supply chain, or OT (Operational Technology) environments.
• Proven ability to optimise large language models for maximum throughput and low latency, with experience deploying AI into highly secure, on-premises, or edge environments.

Good to Have:

• The ability to lead large, complex projects from design through deployment.
• Experience with high-scale machine learning environments, distributed computing, and optimising model inference latency.
• Translating complex ML concepts into business insights for stakeholders.