Principal AI/ML Engineer​

making. As a recognized subject matter expert in the field, this job partners to develop the organization's artificial intelligence strategy, leads implementation of related platforms and products to drive business value and encourages a culture of compliance to governance framework. This job finds opportunities to improve processes and leads efforts to ensure quality of the outcomes and measurable business impact.

Key Accountabilities

• Technical Strategy & Architecture:
  -Define multi-year AI Ops roadmap covering model experimentation, HPC scheduling, inference serving, and data lineage.
  -Evaluate and integrate best-fit OSS/commercial tooling

• Platform Engineering & Operations
  -Build and maintain CI/CD pipelines for model training (GPU/CPU), feature engineering, and automated testing across cloud and HPC clusters.
  -Implement scalable vector databases and caching layers to support low-latency GenAI workloads.
  -Tune scheduler policies for optimal occupancy and cost.
  -Monitor system performance, investigate escalations, and lead post-incident reviews.

• Governance & Compliance
  -Champion data-privacy, model-risk-management, and export-control requirements; embed policy checks into pipelines.
  -Deliver audit-ready documentation for SOC-2, ISO 27001, and sector-specific regulations (e.g., food safety, trade compliance).

• Leadership & Mentoring
  -Coach senior engineers and data scientists on scalable MLOps/HPC patterns.
  -Lead AI Platform Design Reviews and contribute to internal communities of practice.

Qualifications

• Minimum requirement: 6 years relevant experience.

• Typical requirement: 10 + years in software/data engineering with at least 4 years running production ML or HPC workloads and 2 + years in GenAI/LLMOps. Experience leading cross-functional engineering teams preferred.

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Equal Opportunity Employer, including Disability/Vet.