Principal AI/ML Engineer

Primary Responsibilities

• Serve as the technical leader for major commercial initiatives, driving the architecture, design, and implementation of enterprise-scale AI solutions that transform business processes, operations, and customer experiences.
• Lead the development of Agentic AI and autonomous workflow platforms that automate complex business processes, orchestrate enterprise systems, and enable intelligent decision-making at scale.
• Design and implement advanced Generative AI solutions leveraging Large Language Models (LLMs), Retrieval Augmented Generation (RAG), prompt orchestration, vector databases, knowledge graphs, and AI agents.
• Architect and build enterprise-grade multi-agent systems utilizing Agent-to-Agent (A2A) communication patterns, Model Context Protocol (MCP) standards, tool integration frameworks, and intelligent workflow orchestration capabilities.
• Drive AI-powered workflow automation initiatives by integrating AI agents with business applications, APIs, legacy systems, databases, and enterprise platforms to modernize critical operational processes.
• Establish best practices for prompt engineering, prompt optimization, evaluation frameworks, grounding strategies, context management, and response quality assurance across AI applications.
• Lead the fine-tuning, adaptation, optimization, and deployment of foundation models and domain-specific LLMs to improve business relevance, response quality, and operational effectiveness.
• Design and implement scalable AI/ML platforms supporting model training, inference, evaluation, observability, monitoring, governance, security, and lifecycle management.
• Collaborate with product managers, business stakeholders, architects, and engineering teams to identify modernization opportunities and translate business requirements into AI-driven solutions with measurable outcomes.
• Provide technical leadership and mentorship to AI/ML engineers, software engineers, and architects by conducting architecture reviews, code reviews, design workshops, and technical coaching.
• Establish LLMOps, MLOps, and AI governance frameworks covering model lifecycle management, automated testing, evaluation pipelines, deployment automation, performance monitoring, and continuous improvement.
• Drive adoption of AI engineering standards for responsible AI, model safety, security, explainability, auditability, privacy protection, and regulatory compliance.
• Architect hybrid-cloud AI solutions across Azure and Google Cloud Platform (GCP) environments, enabling scalable and cost-effective deployment of enterprise AI services.
• Evaluate emerging AI technologies, frameworks, foundation models, and industry trends, providing technical recommendations that accelerate modernization and innovation initiatives.