Associate Director, AI Application Engineering

About the role

Grade Level (for internal use): 12 S&P Global Energy 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

Responsibilities

• 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
• Success Metrics (First 12 Months)
• Deliver AI-driven applications to production, meeting adoption and performance KPIs
• Publish a library of reusable AI architecture patterns and component accelerators
• Reduce average time-to-production for new AI features by 25% through improved engineering practices
• Grow and coach an engineering team achieving high satisfaction and retention scores
• Establish LLMOps monitoring and observability across all deployed AI models
• Basic Required Qualifications
• 8+ years of experience in software engineering or data engineering, with 3+ years focused on AI/ML application development
• Hands-on experience building and deploying LLM-powered applications and multi-agent systems in production
• Solid expertise in the modern AI/ML engineering stack
• Strong data modelling skills across relational, NoSQL, vector, and graph paradigms
• Experience with cloud-native architectures on at least one major platform (AWS, Azure, or GCP)
• Demonstrated ability to lead small to medium-sized engineering teams
• Strong communication skills able to present technical designs to both engineering and business audiences
• Additional Preferred Qualifications
• Experience with enterprise integration platforms and connecting AI solutions to ERP/CRM/ITSM systems
• Familiarity with AI governance, responsible AI frameworks, and compliance requirements
• Background in applying AI within specific verticals: financial services, retail, healthcare, or manufacturing
• Contributions to open-source AI tooling, technical blog posts, or conference pres

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