Principal AI Engineer — ML / MLOps Platform Architect

Job Description – Principal Machine Learning Engineer (LLM, Agentic AI & Model Platform) Position Summary The Principal Machine Learning Engineer is a senior technical leader responsible for architecting, building, and operationalizing enterprise-scale AI, Generative AI, and Agentic AI capabilities across Databricks, Azure AI Foundry, and cloud-native AI platforms. This role will lead the strategy, architecture, and implementation of foundation models, custom models, AI platform services, ModelOps, and Agentic AI frameworks that power enterprise AI solutions. The ideal candidate combines deep machine learning expertise with hands-on software engineering, cloud architecture, MLOps, and platform engineering skills. They will drive model lifecycle management, AI gateway architecture, model routing strategies, token optimization, and enterprise AI governance while enabling secure, scalable, and cost-efficient AI adoption across the organization.

Enterprise LLM Platform Leadership Own the enterprise strategy for foundation models, frontier models, and custom enterprise models. Evaluate, benchmark, onboard, and operationalize leading AI models from OpenAI, Anthropic, Google, Azure AI Foundry, Databricks Mosaic AI, and open-source ecosystems. Define model selection and deployment strategies based on performance, cost, security, latency, and business requirements. Establish enterprise standards for model consumption and governance. Model Lifecycle Management (ModelOps) Architect and implement end-to-end model lifecycle management capabilities. Lead model training, fine-tuning, evaluation, testing, deployment, monitoring, optimization, and retirement processes. Build automated ModelOps and MLOps pipelines to support enterprise-scale AI workloads. Implement model versioning, lineage, experimentation tracking, model monitoring, and drift detection frameworks. Ensure reproducibility, compliance, governance, and auditability of AI models. Agentic AI Architecture Design and implement enterprise Agentic AI architectures and frameworks. Develop multi-agent orchestration patterns, planning frameworks, tool integration, reasoning workflows, memory management, and contextual intelligence capabilities. Define AgentOps standards for deployment, monitoring, evaluation, and governance of autonomous agents. Establish reusable enterprise frameworks supporting scalable agent development and deployment. AI Gateway & Model Routing Architect enterprise AI Gateway capabilities for secure model access and governance. Design LLM routing frameworks that dynamically select optimal models based on workload, cost, latency, and performance requirements. Define model consumption patterns for applications, APIs, copilots, and intelligent agents. Enable centralized access, governance, monitoring, and observability across all AI services. Develop abstraction layers supporting seamless integration of multiple foundation models. Token Optimization & AI FinOps Define token optimization strategies to improve AI cost efficiency and performance. Implement prompt engineering, caching, model tiering, response optimization, and intelligent routing techniques. Establish monitoring and reporting frameworks for model utilization, token consumption, and AI infrastructure costs. Drive AI FinOps initiatives and platform optimization strategies. AI Platform Engineering Build and scale AI platform capabilities on Databricks, Azure AI Foundry, and Azure cloud platforms. Architect enterprise-ready model serving, inference, vector search, RAG, and semantic retrieval solutions. Develop reusable AI platform services, accelerators, SDKs, and reference architectures. Enable secure and governed AI consumption across multiple business domains. Cloud Infrastructure & Security Design cloud-native infrastructure for model training, fine-tuning, and large-scale inference workloads. Build GPU-enabled, highly scalable, resilient, and secure AI environments. Implement