Principal Engineer - AI / ML Systems

We are looking for a Principal Engineer to own the technical architecture for context services. You will set the system-level direction that bridges our AI engineers and the central platform engineering team, converting complex analytical solutions into highly reliable, production-grade software services at scale. This is the senior-most technical voice in the group, responsible for architecture decisions that other engineers build against.

Responsibilities:

• Architecture and Scale: Define the system architecture for Context Services end-to-end; design low-latency APIs/services supporting thousands of concurrent users with sub-second response times.
• Cross-Functional Leadership: Partner with product, architecture, and engineering leadership to translate ambiguous industrial data problems into scalable, sequenced technical strategy.
• AI and LLM Orchestration: Architect the frameworks for model serving, vector databases, and agentic workflows, including reliable state management, prompt tracking, and orchestration-layer design.
• Platform Integration: Serve as the primary technical liaison with the central Platform team, defining how Context Services leverages and extends core infrastructure.
• Technical Standards and Mentorship: Set engineering standards and review practices for the team; mentor senior- and mid-level engineers; unblock the hardest cross-cutting technical problems.
• Innovation: Drive technical innovation through research, prototyping, and open-source contributions where relevant.

Requirements:

• Bachelor's or master's degree in computer science, data science, or a related field (a PhD is often preferred at this level).
• 10+ years of industry experience in machine learning and software development.
• Strong programming skills in Python, C++, or Java, with deep proficiency building high-concurrency, asynchronous applications and REST/gRPC APIs at scale.
• Expertise in frameworks like PyTorch, TensorFlow, Keras, or Scikit-learn.
• Solid understanding of containerization (Docker) and deployment environments (Kubernetes, cloud platforms); experience architecting for fault tolerance and reliability.
• 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.
• Experience architecting modern lakehouses (e. g., Delta Lake, Apache Iceberg) to process and manage massive, complex datasets specific to manufacturing, supply chain, or OT environments.
• Proven ability to optimize large language models for maximum throughput and low latency, with experience deploying AI into highly secure, on-premises, or edge environments.
• Strong familiarity with the broader AI lifecycle model, i. e., registries, vector/graph databases, LLM evaluation frameworks, and agentic orchestration.

What Sets This Role Apart:

• The ability to lead large, complex projects from design through deployment and to define the architecture that other engineers execute against.
• Experience with high-scale ML environments, distributed computing, and optimizing model inference latency.
• Proven ability to translate complex ML/systems concepts into business insights for stakeholders and leadership.