Associate Director – BI Reporting and AI Engineering Principal

Overview

The AI Engineering Principal is responsible for leading the design, development, deployment, and scaling of enterprise AI and GenAI solutions. This role drives the end-to-end delivery of AI products, agentic applications, intelligent automation capabilities, and analytics solutions that create measurable business value.

The position works closely with business stakeholders, product managers, data scientists, data engineers, and platform teams to transform AI use cases into production-ready solutions. The ideal candidate combines strong software engineering practices with deep expertise in AI/ML, GenAI, cloud platforms, and enterprise architecture.

Responsibilities

AI Solution Delivery

• Lead the design, development, and deployment of AI, ML, and Generative AI solutions.
• Build scalable AI applications, copilots, intelligent assistants, and agent-based systems.
• Translate business use cases into production-ready AI products and services.
• Drive technical decisions across architecture, frameworks, tools, and deployment patterns.

AI Platform & Engineering

• Establish reusable AI engineering frameworks, accelerators, and best practices.
• Drive adoption of AI platforms, LLMs, vector databases, RAG architectures, and agent frameworks.
• Develop robust API, microservices, and integration patterns supporting AI workloads.
• Ensure solutions are scalable, secure, resilient, and cost optimized.

MLOps / LLMOps

• Define standards for model lifecycle management, monitoring, observability, and governance.
• Implement CI/CD pipelines for AI applications and machine learning models.
• Establish evaluation frameworks for LLM quality, safety, performance, and reliability.
• Drive responsible AI, security, compliance, and risk management practices.

Technical Leadership

• Provide technical direction and mentorship to AI engineers and cross-functional teams.
• Lead architecture reviews and engineering governance discussions.
• Evaluate emerging AI technologies and identify opportunities for innovation.
• Build engineering roadmaps aligned with enterprise AI strategy.

Stakeholder Management

• Collaborate with product, business, analytics, and technology leaders to prioritize initiatives.
• Communicate technical concepts effectively to executive and non-technical audiences.
• Manage delivery expectations, risks, dependencies, and outcomes across multiple projects.

Qualifications

Education

• Bachelor's degree in Computer Science, Engineering, Data Science, Information Systems, or related field.
• Master's degree preferred.

Experience

• 14+ years of software engineering, data engineering, or AI/ML engineering experience.
• 10+ years leading enterprise-scale AI, Machine Learning, or Generative AI initiatives.
• Proven experience delivering AI products from ideation through production deployment.
• Experience working with global teams and enterprise stakeholders.

Technical Skills

• Strong programming skills in Python and modern engineering frameworks.
• Experience with Large Language Models (OpenAI, Azure OpenAI, Anthropic, Gemini, etc.).
• Expertise in RAG, agentic AI systems, prompt engineering, and AI 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, and microservices architectures.
• Knowledge of MLOps, LLMOps, CI/CD, model monitoring, and governance frameworks.

Leadership Skills

• Strong strategic and systems-thinking mindset.
• Excellent stakeholder management and influencing skills.
• Ability to mentor and grow high-performing engineering teams.
• 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, and Responsible AI practices.
• Experience building reusable AI engineering capabilities across multiple business domains.