Director, Applied AI Engineering

ICW Group•San Diego, CA

2d•$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. It owns AI Platforms as a shared enterprise capability, including managed foundation model services, agent runtime and orchestration, and machine learning operations. The Director sets engineering standards and establishes how AI work is delivered and supported in production, partnering with data services, cloud and infrastructure engineering, product management, enterprise risk, and governance functions. The position oversees the design, development, production delivery, and support of AI solutions, including generative AI applications, intelligent document processing, conversational interfaces, and agentic and multi-agent workflows. It operates AI Platforms for engineering, analytics, and data science teams, setting standards and guardrails; manages delivery capacity and commitments; and maintains capacity for applied research and technology evaluation. The Director also ensures required risk and governance reviews, collaborates on data and infrastructure needs, and works with financial operations on cost transparency and platform unit economics. As a people leader, the Director provides strategic and tactical direction, manages hiring and onboarding, mentors and develops employees, sets performance expectations, and fosters a collaborative culture focused on innovation, accountability, engagement, and professional growth. The role directly supervises applied AI engineering and AI Platforms employees, supervises through subordinate team leads, and manages a blend of employee and contract resources. The role requires a bachelor's degree in a relevant technical field and at least 10 years of experience in software, data, or machine learning engineering, including at least five years in a leadership role. Experience delivering production generative or agentic AI solutions, operating an AI or machine learning platform, and developing generative AI applications is required. Insurance or regulated financial services experience, a technical or quantitative master's degree, and relevant cloud, machine learning, or Agile certifications are preferred. The position operates in an office environment and involves frequent use of a computer, telephone, copier, and standard office equipment. The employee may need to sit at a computer for a period of time, occasionally reach, stoop, kneel, crouch, or crawl, and lift or move up to 25 pounds. The current base salary range is $160,745.90–$287,738.43, exclusive of fringe benefits and potential bonuses; final compensation depends on candidate-specific factors, including experience, education, and role location.

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 involving multiple teams, stakeholders, and dependencies.
Demonstrated ability to mentor and develop engineering team members and improve team dynamics and performance.
Excellent communication and stakeholder management skills, including the ability to present 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.

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.
Cloud or machine learning certifications, including AWS Certified Machine Learning Engineer, AWS Certified Solutions Architect, Microsoft Certified Azure AI Engineer, or other relevant certifications.
Agile certifications, including Certified Scrum Master (CSM), Certified SAFe practitioner, PMI Agile Certified Practitioner (PMI-ACP), or other relevant certifications.

Responsibilities

Lead 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, onboarding and supporting engineering, analytics, and data science teams and defining standards and guardrails for platform tenants.
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 commitments to business stakeholders.
Partner with data and AI product management to turn a prioritized portfolio into achievable delivery commitments.
Oversee production support for 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 with 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 AI and machine learning solutions complete required review and approval before production deployment.
Collaborate with data services teams to define and secure data products, semantic models, and pipelines needed for AI solutions.
Partner with cloud and infrastructure engineering to define compute, hosting, security, and scaling requirements for AI Platforms.
Partner with financial operations on cost transparency and unit economics for AI Platforms, including token, inference, agent execution, and model cost management.
Translate 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 decisions; monitor engineering performance and make necessary adjustments.
Provide strategic and tactical direction, communicate organizational mission and values, and set performance standards.
Mentor and develop team members, fostering innovation, collaboration, accountability, and professional growth.
Build a collaborative team culture that supports engagement, recognition, retention, continuous development, and high performance.
Manage hiring, onboarding, and training; set expectations and develop career paths for a skilled and resilient team.
Interview, hire, train, assign, and direct work; conduct performance and salary reviews; reward and discipline employees; address complaints; resolve problems; coach and mentor staff; create and monitor development plans; forecast staffing needs; and enforce department policies and procedures.
Supervise directly through subordinate team leads and manage a blend of employee and contract resources.