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.

Experience
12-16 yrs