Director, Applied AI Engineering

ICW Group • San Diego, CA

4d • $160,746 - $287,738

About The Position

The Director of Applied AI Engineering is a senior leader responsible for delivering and operating generative and agentic AI solutions across the enterprise. The role leads engineers who design, build, deploy, and support AI capabilities for business areas including underwriting, claims, distribution, finance, and corporate operations.

The Director owns AI Platforms as a shared enterprise capability, including managed foundation model services, agent runtime and orchestration, and machine learning operations. This includes establishing engineering standards and the operating model for developing and supporting AI solutions, and onboarding and supporting engineering, analytics, and data science teams that use the platform.

The role works closely with data services, cloud and infrastructure engineering, product management, financial operations, and enterprise risk and governance functions. It ensures AI solutions are appropriately reviewed, responsibly deployed, reliable, and sustainable in production while balancing applied research with delivery commitments. The Director also provides strategic and tactical leadership, supervises and develops team members, and manages hiring, onboarding, training, performance, and staffing needs.

Requirements

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related field.
• At least 10 years of experience in software, data, or machine learning engineering.
• At least 5 years of experience in a leadership role.
• Experience delivering and operating generative or agentic AI solutions in production environments, beyond pilots or proofs of concept.
• Experience owning or operating an AI or machine learning platform, including model deployment, versioning, and production monitoring.
• Experience with generative AI application development, including retrieval-augmented generation, agentic and multi-agent workflows, and model evaluation methods.
• Strong working knowledge of generative and agentic AI architectures, foundation model selection, model lifecycle management, and evaluation methods.
• Working knowledge of managed foundation model and agent runtime services, including Amazon Bedrock and Bedrock AgentCore or comparable platforms, and data engineering practices that support retrieval and grounding.
• Strong project management skills and ability to manage complex engineering initiatives with multiple teams, stakeholders, and dependencies.
• Demonstrated ability to mentor and develop engineering team members, with a focus on team dynamics and performance.
• Excellent communication and stakeholder management skills, including presenting technical information to technical and non-technical stakeholders.
• Ability to operate effectively when critical dependencies are owned by other teams, working through published commitments and shared prioritization.
• Proven ability to manage cross-functional teams and drive change in complex environments.
• Strong analytical skills to assess business and technical requirements and develop effective solutions.
• Ability to carry out supervisory responsibilities, including interviewing, hiring, training, planning and assigning work, conducting performance and salary reviews, coaching, resolving problems, and forecasting staffing needs.

Nice To Haves

• Experience in insurance or another regulated financial services environment with model governance requirements.
• Master's degree in a technical or quantitative discipline.
• Relevant cloud or machine learning certifications, such as AWS Certified Machine Learning Engineer, AWS Certified Solutions Architect, or Microsoft Certified Azure AI Engineer.
• Relevant Agile certifications, such as Certified Scrum Master (CSM), Certified SAFe practitioner, or PMI Agile Certified Practitioner (PMI-ACP).

Responsibilities

• Lead the design, development, and production delivery of enterprise generative and agentic AI solutions, including generative AI applications, intelligent document processing, conversational interfaces, and agentic and multi-agent workflows.
• Own AI Platforms, including managed foundation model services, agent runtime and orchestration, and machine learning operations such as model registry, feature engineering, deployment pipelines, model serving, versioning, evaluation, and production monitoring.
• Operate AI Platforms as a shared enterprise capability; onboard and support engineering, analytics, and data science teams as platform tenants and define standards and guardrails for their work.
• Establish and maintain engineering standards for generative and agentic AI development, including prompt and context management, evaluation frameworks, testing, documentation, and release management.
• Direct the delivery model for generative and agentic AI work, including team structure, capacity planning, and management of commitments to business stakeholders.
• Partner with data and AI product management to convert the prioritized portfolio into achievable delivery commitments.
• Oversee production support for generative and agentic AI solutions, ensuring reliability, performance, and issue resolution within established service levels.
• Maintain capacity for applied research and evaluation of emerging AI capabilities while balancing exploratory work against production delivery commitments.
• Evaluate emerging AI technologies, foundation models, platforms, and vendors; make build-versus-buy recommendations and recommend adoption when appropriate.
• Partner with enterprise risk and governance functions to ensure generative, agentic, and machine learning solutions complete required review and approval before production deployment.
• Collaborate with data services teams to define and secure the data products, semantic models, and pipelines required for AI solutions.
• Partner with cloud and infrastructure engineering teams to define the compute, hosting, security, and scaling requirements on which AI Platforms depend.
• Partner with financial operations on cost transparency and unit economics for platform consumption, including token, inference, agent execution, and model cost management.
• Translate complex cross-functional business and integration needs into actionable development requirements, designs, and plans for engineering teams.
• Direct the team using KPIs and data to inform decision-making; monitor and evaluate engineering performance and make necessary adjustments.
• Provide strategic and tactical direction, communicate organizational mission and values, and set standards for performance excellence.
• Mentor and develop engineering team members and foster innovation, collaboration, accountability, engagement, recognition, retention, and professional growth.
• Manage hiring, onboarding, and training; set clear expectations and develop career paths and staffing plans.
• Supervise employees and subordinate team leads in accordance with company policies and applicable laws, including assigning and directing work, conducting performance and salary reviews, addressing complaints, coaching and developing staff, monitoring development plans, setting goals, and enforcing department policies and procedures.
• Manage a blend of employee and contract resources.

Benefits

• Competitive benefits package with medical, dental, and vision plans.
• 401(k) retirement plan with company match.
• Bonus potential for all positions.
• Paid time off.
• Paid holidays throughout the calendar year.
• Company support for continued learning.

What This Job Offers

• Job Type: Full-time
• Career Level: Director