Director, Machine Learning Engineering, AI Studio

Role Description:

Director, Machine Learning Engineering role offers a unique opportunity to join a high-impact engineering organization within AI Data Science (AID). We are the Applied AI team (AI Studio). AI Studio is Amgens enterprise engine for turning high-value business challenges into scalable AI products. We partner with business and technology leaders across the company to identify the right opportunities, shape them into actionable use cases, and design, build, launch, operate and scale AI products responsibly. In this role, you will own multi-department Machine Learning and automation Delivery for AI Studio leading a number of Digital product teams that deliver high value solutions and support them once delivered. You will also lead key automation platforms with lifecycle direction and operational support. You will build high-performing leadership systems and ensure that AI/ML investments deliver scientifically credible, operationally reliable, responsibly governed and measurable outcomes across multiple departments or product areas.

.

Roles Responsibilities:

• Own multi-department AI/ML engineering strategy aligned to business, scientific and enterprise technology priorities; define multi-year investment logic, portfolio outcomes, capability priorities and major-change programs.
• Set portfolio direction and execution for production software, data/knowledge systems, statistical modeling, classical ML, deep learning, foundation models, GenAI, RAG, agents, evaluation, MLOps/LLMOps, cloud and AI operations.
• Drive delivery of AI solutions within agreed timelines, budget, and quality parameters.
• Mentor engineers and architects in advanced AI development, fostering technical depth and innovation.
• Build and lead high-performing leadership systems across departments; establish clear accountabilities for Associate Directors, managers and Principal engineers, allocate resources, strengthen succession and develop organizational capability.
• Establish multi-department standards for modeling evidence, experiment design, uncertainty, robustness, evaluation, lifecycle quality, reliability, model/vendor selection, reusable capabilities and platform interoperability.
• Adhere to governance and compliance processes specific to agentic AI to promote safe, ethical, and effective system adoption.
• Partner with Responsible AI and AI/ML Platforms to set posture on Responsible AI and provide thought leadership on Governance forums.
• Executive leadership to a global team of Product Analysts, Platform and DevOps Engineers contributing to a growing internal AI Automation Platforms and Solutions management practice.
• Own portfolio scorecards and investment trade-offs across scientific or business impact, model/decision quality, adoption, reliability, latency, cost, risk, reuse, support burden, delivery capacity and technical debt.
• Sponsor major platform, operating-model and technology changes, including enterprise knowledge and retrieval strategy, model/agent gateways, evaluation services, lifecycle platforms, vendor strategy, build-versus-buy and credible model/technology-exit paths.
• Represent AI/ML engineering and delivery strategy relative to AI studio with senior management and strategic partners, build cross-department alliances, resolve portfolio-level conflicts and ensure leaders are accountable for outcomes, governance, talent health and continuous improvement.

Basic Qualifications and Experience:

• Degree in Computer Science, Engineering, or related field with 18-23 years experience
• Preferred certifications: Cloud Platform Architect (AWS, Azure, GCP), ML and GenAI certifications.

Experience must demonstrate Director-level scope consistent with the responsibilities above.

Functional Skills:

• Multi-department AI/ML/Automation and architecture strategy: Portfolio direction across production software, data, models, retrieval, agents, workflows, cloud, platform interoperability, modernization and lifecycle operating models.
• Modeling, statistics and decision-quality strategy: Enterprise expectations for experimentation, causal/statistical assumptions, calibration, uncertainty, robustness, subgroup analysis, interpretability, error costs and decision-aware evidence.
• Foundation models, GenAI, RAG and agent strategy: Model/vendor strategy, advanced-model adoption, knowledge architecture, retrieval and provenance, agent autonomy, tool governance, human oversight, evaluation…