Applied AI ML and Context Engineer - Lead

Applied Ai And Machine Learning Lead

Build what's next in enterprise AI—solutions that materially improve how teams make decisions, automate work, and serve internal customers. In this role, you will take generative AI from concept to production and help set the standard for semantic consistency across systems. You will partner closely with stakeholders to turn complex business needs into measurable outcomes. You will mentor talent and influence technical direction across Corporate Technology and supported Corporate Functions. If you enjoy solving hard problems with real impact, this is the opportunity.

As an Applied AI and Machine Learning Lead in the Corporate Technology Data Science and AI team, you will design, build, and deploy scalable analytical and generative AI solutions that deliver measurable business value. You will collaborate with stakeholders to shape problem statements, define success metrics, and deliver production-ready models and intelligent workflows. You will help establish enterprise semantic modeling standards and a unified semantic layer that improves trust and consistency across analytics and AI use cases. We will support you with a collaborative environment where you can innovate, mentor others, and continuously grow your skills.

Job Responsibilities

• Develop generative AI, agentic AI, and large language model solutions in Python from proof-of-concept through production deployment.
• Design context engineering approaches to improve model accuracy, latency, reliability, and overall performance.
• Lead enterprise semantic modeling strategy, including ontology standards, governance, and lifecycle management.
• Create scalable enterprise ontologies that model business entities, relationships, rules, and constraints in partnership with domain experts.
• Define semantic integration patterns across data pipelines, application programming interfaces, data contracts, and experience layers to resolve semantic conflicts.
• Build and govern a unified semantic layer that enables trusted analytics across business intelligence, machine learning, and transactional systems.
• Enable intelligent workflows and AI agents using ontology-driven context, semantic reasoning, and orchestration methods.
• Build and maintain data pipelines and frameworks for model training, evaluation, optimization, and production operations.
• Implement responsible AI practices, model risk controls, and governance aligned to regulated environments.
• Communicate complex technical concepts clearly to technical and non-technical stakeholders, including senior leaders, to align outcomes to business objectives.
• Mentor engineers and data scientists and promote modern machine learning engineering best practices and continuous improvement.

Required Qualifications, Capabilities, and Skills

• Master's degree in a data science-related discipline and 8 years of industry experience, or PhD in a data science-related discipline.
• Experience in data analysis, transformation, and analytics using Python.
• Demonstrated ability to develop and maintain production-quality code.
• Experience with continuous integration and unit test development.
• Strong written and verbal communication skills for technical and business audiences.
• Demonstrated scientific thinking and structured problem-solving skills.
• Ability to work independently and collaboratively in a team environment.
• Experience building and managing data pipelines and processing workflows.
• Track record of delivering actionable insights from data.
• Demonstrated attention to detail, curiosity, and ownership in complex analytical work.
• Commitment to continuous learning and professional growth in AI and machine learning.

Preferred Qualifications, Capabilities, and Skills

• Familiarity with the financial services industry.
• Experience with A/B testing and data-driven product development.
• Knowledge of cloud-native deployment in large-scale distributed environments.
• Experience developing and debugging production-quality machine learning code.
• Exposure to prompt engineering practices for large language models.
• Exposure to orchestration libraries and frameworks for large language model applications.
• Experience implementing machine learning solutions in business environments.