Principal AI Engineer — ML / MLOps Platform Architect

Position summary

Enterprise AI platform and model operations

• Own strategy for foundation, frontier, and custom enterprise models; evaluate, benchmark, onboard, and operationalize models from OpenAI, Anthropic, Google, Azure AI Foundry, Databricks Mosaic AI, and open-source ecosystems.
• Set model selection, deployment, consumption, and governance standards based on performance, cost, security, latency, and business requirements.
• Architect end-to-end model lifecycle management and lead model training, fine-tuning, evaluation, testing, deployment, monitoring, optimization, and retirement.
• Build ModelOps and MLOps pipelines, versioning, lineage, experimentation tracking, drift detection, reproducibility, compliance, governance, and auditability.

Agentic AI, gateways, and optimization

• Design Agentic AI architectures, multi-agent orchestration, planning, tool integration, reasoning workflows, memory management, and contextual intelligence.
• Define AgentOps standards and reusable frameworks for deploying, monitoring, evaluating, and governing autonomous agents.
• Architect secure AI gateways and model routing; enable governed access, observability, and integration across applications, APIs, copilots, and agents.
• Define token optimization strategies and implement prompt engineering, caching, model tiering, response optimization, routing, and AI cost reporting.

Platform, infrastructure, and leadership

• Build AI platform capabilities on Databricks, Azure AI Foundry, and Azure, including model serving, inference, vector search, RAG, and semantic retrieval.
• Develop reusable platform services, accelerators, SDKs, and reference architectures for governed AI use across business domains.
• Design cloud infrastructure for training, fine-tuning, and large-scale inference, including secure, resilient, scalable GPU-enabled environments.
• Implement Infrastructure as Code, automated deployments, observability, access controls, secrets management, model security, and responsible AI controls.
• Mentor AI, ML, and platform engineers and architects; lead architecture reviews, technology evaluations, proof-of-concepts, and AI transformation initiatives.

Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field.
• 12+ years of experience in Software Engineering, Machine Learning, AI Platform Engineering, or Cloud Architecture.
• 5+ years of experience leading enterprise AI/ML platform implementations.
• Proven experience deploying and operating large-scale AI, LLM, and Agentic AI solutions in production environments.

Success measures

Inclusion commitment