Purpose of the Role
To drive technical excellence and innovation by leading the design and implementation of robust software solutions, providing mentorship to engineering teams, fostering cross functional collaboration, and contributing to strategic planning to ensure the delivery of high quality solutions aligned with business objectives.
Accountabilities
Provide guidance and expertise to engineering teams to ensure alignment with best practices and foster a culture of technical excellence.
Contribute to strategic planning by aligning technical decisions with business goals, anticipating future technology trends, and providing insights to optimize product roadmaps.
Design and implement complex, scalable, and maintainable software solutions, considering long term viability and business objectives.
Mentor and coach junior and mid level engineers to foster professional growth and knowledge sharing, elevating the overall skill set and capabilities of the organization.
Collaborate with business partners, product managers, designers, and other stakeholders to translate business requirements into technical solutions and ensure a cohesive approach to product development.
Innovate within the organization by identifying and incorporating new technologies, methodologies, and industry practices into the engineering process.
Director Expectations
Manage or lead large teams, navigate strategic projects, and act as a deep technical expert and thought leader.
Train, guide, and coach less experienced specialists, provide expert advice to senior management and committees, and influence decisions outside the function.
Manage budgeting, resourcing, and policy creation for significant sub functions, ensuring compliance and governance.
Monitor the external environment, regulators, and advocacy groups to influence on behalf of the organization.
Key Accountabilities
- Accountable for the production readiness of all AI/Gen AI systems – every model, pipeline, and agent that goes live meets defined SLAs for latency, accuracy, cost, security, and observability before release.
- Accountable for the AI platform architecture – the shared foundations (model gateway, embedding stores, prompt management, evaluation framework) are coherent, scalable, and enable teams to ship AI features without reinventing infrastructure.
- Accountable for AI risk and compliance posture – all deployed AI systems have appropriate guardrails, audit trails, and governance controls that satisfy regulatory and internal model risk requirements.
- Accountable for technical quality across AI engineering teams – code review standards, testing practices, LLM Ops maturity, and architectural consistency are maintained across squads, not just within one team.
- Accountable for the AI technical roadmap – emerging capabilities are continuously evaluated, strategic bets are made on the right models/tooling/patterns, and the organization is positioned to adopt new AI techniques without costly re architecture.
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