Associate Director, AI Application Engineering

Role Overview

• 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
• 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
• 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
• 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

Requirements

• 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.

Tech Stack

• AWS
• Azure
• Cloud
• Google Cloud Platform
• Microservices
• NoSQL

Benefits

• Health & Wellness: Health care coverage designed for the mind and body.
• Flexible Downtime: Generous time off helps keep you energized for your time on.
• Continuous Learning: Access a wealth of resources to grow your career and learn valuable new skills.
• Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company-matched student loan contribution, and financial wellness programs.
• Family Friendly Perks: It’s not just about you. S&P Global has perks for your partners and little ones, too, with some best-in class benefits for families.
• Beyond the Basics: From retail discounts to referral incentive awards—small perks can make a big difference.