Amgen
Website:
amgen.com
Company:
https://www.linkedin.com/company/amgen
Seniority: Not Applicable
Industries: Biotechnology Research and Pharmaceutical Manufacturing
Job details:
About Amgen
Amgen harnesses the best of biology and technology to fight the world’s toughest diseases and make people’s lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains at the cutting edge of innovation, using technology and human genetic data to push beyond what is known today.
About The Role
Role Description:
Director, Machine Learning Engineering role offers a unique opportunity to join a high-impact engineering organization within AI & Data Science (AI&D). We are the Applied AI team (AI Studio). AI Studio is Amgen’s 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, capability boundaries and responsible scaling.
- AI evaluation, MLOps/LLMOps and reliability strategy: Shared evaluation systems, gold-set governance, registries, lineage, CI/CD, release governance, observability, drift/quality monitoring, incidents, capacity, FinOps, updates, support and retirement.
- Portfolio value, organizational leadership and major change: AI investment logic, KPI/scorecard design, adaption, ROI/value, reusable capability strategy, resource allocation, leadership systems, succession, executive communication and major transformation.
- Operationalization of Production grade automation/AI/ML workflows: Strong experience in not just building but leading teams that operationalize workflows across Automation and AI/ML so that AI studio can build and operate digital products
Must-Have Skills:
- Demonstrated leadership of advanced AI/ML portfolios or major programs spanning multiple departments or product areas, with measurable scientific, operational or business impact.
- Expert strategic judgment across production ML/AI, with sufficient technical depth to govern modeling, GenAI, evaluation, data/knowledge, lifecycle, platform, governance and vendor decisions through senior technical leaders.
- Proven experience in managing large-scale tech projects and delivering innovative AI/ML solutions.
- Proficient with AI/ML libraries and frameworks (e.g., Keras, PyTorch, scikit-learn, LangChain, llamaindex).
- Proven ability to build high-performing leadership teams, allocate resources, develop succession, hold leaders accountable and deliver through Associate Directors, managers and Principal engineers.
- Strong executive influence across business/scientific leadership, product, enterprise architecture, data/platform, security, privacy, Responsible AI, Quality, GxP, Finance and operations.
Good-to-Have Skills:
- Experience leading enterprise GenAI, RAG, agentic, multimodal, Applied ML, evaluation or MLOps/LLMOps strategy across multiple business or scientific domains.
- Experience setting model/vendor strategy across hosted and self-managed models, routing/cascades, specialized models, PEFT/LoRA, quantization or inference optimization with clear quality, privacy, cost and exit criteria.
- Understanding and exposure to Intelligent Automation with experience in automation tools like UiPath, Process Intelligence tools like Celonis, DocuSign and GenAI Tools/LLMs.
- Experience directing enterprise knowledge architecture, graph/vector retrieval, model/agent gateways, evaluation platforms, agent/tool governance, policy enforcement and continuous risk monitoring.
- Experience with AWS, Bedrock/SageMaker, Databricks, Spark, Kubernetes, shared lifecycle platforms, observability, capacity planning, FinOps and enterprise AI platform governance.
- Experience in biotechnology, pharmaceutical, healthcare, GxP, validated or another highly regulated global enterprise environment; recognized leadership in responsible production AI.
- Strong knowledge of Agile methodologies and product management principles
- Strong knowledge of IT service management (ITSM) principles and methodologies
Soft Skills:
- Enterprise strategic thinking and ability to connect AI/ML investment to scientific, operational and business outcomes across multiple departments.
- Executive presence and trusted influence across senior management, scientific/business leaders and technical communities.
- Organizational leadership that creates clear accountability, psychological safety, succession depth and sustainable performance across multiple departments.
- Evidence-based judgment under ambiguity, including the willingness to reject technically impressive approaches that do not improve outcomes safely or economically.
- Ability to lead major change while balancing innovation, evidence, risk, privacy, compliance, cost, adoption, talent and long-term maintainability.
EQUAL OPPORTUNITY STATEMENT
Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law.
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