The Role- Associate Director, AI Application Engineering. The Team: We are looking for a highly motivated, enthusiastic, and skilled engineering leader for S&P Global Energy. We strive to deliver solutions that are sector-specific, data-rich, and hyper-targeted for evolving business needs. The Impact: S&P Global Energy is seeking a Associate Director of AI Application Engineering who is a senior technical role that bridges hands-on AI engineering with architectural leadership. This individual will design, build, and govern AI-driven enterprise applications with particular focus on agentic AI solutions, robust data modelling, and scalable system architecture. The role requires both deep technical execution capability and the ability to lead engineering teams and guide technical strategy

AI Application Design & Engineering

Architect, develop, and deploy AI-driven applications leveraging LLMs, multi-agent systems, RAG, and intelligent automation pipelines

Design agentic AI workflows including agent planning loops, tool orchestration, dynamic memory, and human-in-the-loop controls

Own the technical design and build of AI microservices, APIs, and integration layers connecting AI capabilities to enterprise systems

Lead rapid prototyping and proof-of-concept delivery to validate AI approaches before full-scale implementation

Ensure production AI applications meet performance, scalability, reliability, and security requirements

Data Modelling & AI Data Architecture

Design data models and schemas optimized for AI workloads: feature engineering, vector embeddings, semantic search, and LLM context management

Build and maintain knowledge graphs, ontologies, and entity resolution pipelines supporting intelligent agent reasoning

Define data ingestion, transformation, and enrichment pipelines that feed AI feature stores and inference services

Implement data quality, validation, and drift detection frameworks to maintain model accuracy in production

Collaborate with data platform teams to architect lakehouse patterns, real-time streaming, and batch processing for AI use cases

Technical Architecture & Standards

Design reusable AI solution patterns, reference architectures, and component libraries for enterprise deployment

Lead technical design reviews and architecture assessments for AI projects across the organisation

Define and enforce coding standards, testing practices, and MLOps/LLMOps pipelines for AI application delivery

Evaluate emerging AI frameworks, tooling, and models — providing structured technical recommendations

Ensure alignment with enterprise architecture standards, security policies, and cloud governance guardrails

Team Leadership & Delivery

Lead a team of 4–8 AI/ML engineers and data engineers, providing technical direction, mentorship, and career development

Manage the delivery of AI application workstreams: planning, estimation, risk management, and quality assurance

Partner with product managers, data scientists, and business analysts to translate requirements into technical designs

Champion engineering best practices: code review culture, test-driven development, observability, and documentation standards