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.