Associate Director - BI Reporting and AI Engineering Principal

Role overview

AI delivery and platform engineering

• Lead AI, ML, and Generative AI solution delivery and build scalable applications, copilots, assistants, and agent-based systems.
• Translate business use cases into production-ready products and drive architecture, frameworks, tools, and deployment decisions.
• Establish reusable engineering frameworks and adoption of AI platforms, LLMs, vector databases, RAG, and agent frameworks.
• Develop APIs, microservices, and integration patterns; ensure solutions are scalable, secure, resilient, and cost-optimized.

MLOps and technical leadership

• Set standards for model lifecycle management, monitoring, observability, governance, and LLM evaluation.
• Implement CI/CD for AI and ML; drive responsible AI, security, compliance, and risk management.
• Mentor engineers, lead architecture reviews, assess emerging technologies, and build engineering roadmaps aligned with enterprise AI strategy.

Stakeholder management

• Collaborate with product, business, analytics, and technology leaders to prioritize initiatives.
• Communicate technical concepts to executive and non-technical audiences and manage project expectations, risks, dependencies, and outcomes.

Education and experience

• Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or a related field; a master's degree is preferred.
• 14+ years in software engineering, data engineering, or AI/ML engineering, including 10+ years leading enterprise AI, ML, or GenAI initiatives.
• Proven end-to-end AI product delivery experience and experience working with global teams and enterprise stakeholders.

Technical qualifications

• Strong Python programming and modern engineering framework skills.
• Experience with LLMs such as OpenAI, Azure OpenAI, Anthropic, or Gemini; expertise in RAG, agentic AI, prompt engineering, and orchestration frameworks.
• Experience with Databricks, Azure AI Services, AWS AI/ML services, or equivalent cloud platforms.
• Strong understanding of vector databases, embeddings, knowledge retrieval, and semantic search.
• Experience with containerization, Kubernetes, APIs, microservices, MLOps, LLMOps, CI/CD, model monitoring, and governance frameworks.

Leadership qualifications

• Strategic and systems thinking, stakeholder management, influencing, mentoring, and team development skills.
• Strong communication and executive presentation capabilities.

Preferred experience

• Enterprise AI platform development; copilot and AI assistant development; agentic AI and autonomous workflow solutions.
• AI governance, security, responsible AI, and reusable AI engineering capabilities across business domains.