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This Principal AI/ML Engineer role involves technical leadership for major commercial initiatives, focusing on architecting, designing, and implementing enterprise-scale AI solutions. You will drive the development of Agentic AI and autonomous workflow platforms, automating complex business processes and enabling intelligent decision-making at scale.

Spearhead the design and implementation of advanced Generative AI solutions, utilizing Large Language Models (LLMs), Retrieval Augmented Generation (RAG), prompt orchestration, vector databases, and AI agents. Architect and build enterprise-grade multi-agent systems with Agent-to-Agent (A2A) communication and Model Context Protocol (MCP) standards. Drive AI-powered workflow automation by integrating AI agents with business applications, APIs, and enterprise platforms. Establish best practices for prompt engineering, optimization, evaluation, and response quality assurance. Lead the fine-tuning, adaptation, and deployment of foundation models and domain-specific LLMs.

Design and implement scalable AI/ML platforms for model training, inference, evaluation, and lifecycle management. Collaborate with stakeholders to translate business requirements into AI-driven solutions. Provide technical leadership and mentorship to engineering teams through reviews and coaching. Establish LLMOps, MLOps, and AI governance frameworks. Drive adoption of responsible AI standards for safety, security, explainability, and privacy. Architect hybrid-cloud AI solutions across Azure and GCP. Evaluate emerging AI technologies to accelerate innovation.

A Bachelor's or Master's degree in Computer Science, AI, Machine Learning, Software Engineering, Data Science, or a related technical field is required. You should possess over 12 years of professional software or AI/ML engineering experience, with at least 5 years focused on AI, ML, and Generative AI solutions. Proven experience designing and delivering enterprise-scale AI/ML platforms and intelligent automation solutions in production is essential.

Hands-on experience building AI agents, multi-agent systems, autonomous workflows, and enterprise automation frameworks is crucial. Solid experience implementing Model Context Protocol (MCP), Agent-to-Agent (A2A) integrations, tool-calling frameworks, and enterprise AI orchestration patterns is expected. Experience with Azure OpenAI, OpenAI APIs, and cloud-native AI services across Azure and GCP is necessary. Deep expertise in Generative AI technologies, including LLMs, RAG, prompt engineering, embeddings, vector databases, and foundation model architectures is required.

Solid understanding of AI platform engineering, distributed systems, microservices, containerization, Kubernetes, and API design is important. Proven ability to influence technical strategy and lead architecture decisions is key. Excellent programming skills in Python and experience with modern AI frameworks like LangChain, LangGraph, and OpenAI SDKs are required. Exceptional communication and stakeholder management skills are necessary to articulate complex AI concepts to diverse audiences.