ML Ops Lead

Job Title: ML Ops Lead

Introduction to role:
Are you ready to build and run agentic AI at global scale to transform how we create, manage, and activate content that reaches patients and healthcare professionals? This role sits at the heart of our enterprise engine—powering efficiency, reliability, and intelligent decision-making across the content lifecycle so our teams can focus on breakthroughs that matter.

As ML Ops Lead, you will own the operational backbone for AI agents and workflows that span creation, approval, and activation. You will turn strategy into working systems, put the right guardrails in place, and deliver measurable improvements in throughput, quality, and personalization. Do you thrive at the intersection of engineering excellence, operational rigor, and responsible AI governance?

Accountabilities:
- Capability Strategy and Roadmap: Build and complete the multi-year vision for agentic AI and MLOps, aligning outcomes to business priorities, risk posture, and global scale. Translate strategy into an operating model and an actionable delivery plan that moves from pilot to production with confidence.
- Team Formation and Leadership: Build, lead, and develop the agent manager team. Define roles, hire top talent, establish career paths, and implement a follow-the-sun support model for true global coverage.
- AI Workflow Deployment and Maintenance: Oversee the full lifecycle of AI agents and workflows across the end-to-end content lifecycle and supply chain. Ensure resilient releases, rapid incident response, and continuous improvement.
- Integration Across the Content Supply Chain: Orchestrate seamless integrations with content platforms, DAM, CRM, and activation channels. Standardize taxonomies and metadata to reduce friction, rework, and cycle time.
- Data Strategy Alignment: Partner to align ontologies, customer data, knowledge graphs, and governance policies to marketing objectives. Enable efficient retrieval-augmented generation and robust, transparent measurement.
- Operational Management: Establish service management practices including task queues, service agreements and objectives, procedural guides, change coordination, and capacity planning. Implement FinOps to control AI compute and inference costs.
- Risk, Compliance, and Responsible AI: Implement privacy, consent, factuality, bias, and safety guardrails. Enforce auditability, human-in-the-loop controls, and adherence to regulations and internal policies.
- Platform Engineering Partnership: Co-own agent platform requirements—prompt assets, tool registries, connectors, feature stores, vector indices, model registries, and observability—in close partnership with technology and business collaborators.
- Standards and Governance: Define policies for prompt and version management, data lineage, content provenance, and incident response. Institute quality gates, evaluation protocols, and rollback procedures.
- Cross-Functional Collaboration: Partner with excellence teams and control functions to prioritize use cases, remove blockers, and drive adoption. Build momentum and clarity across global collaborators.
- Performance and return on investment: Set metrics and dashboards for efficiency, cycle time, reliability, accuracy, personalization uplift, and cost per output. Run experiments and post-implementation reviews to optimize impact.
- Evangelism and Enablement: Offer expert input to playbooks and training. Raise agentic AI literacy and responsible use across communities of practice, with a strong focus on business services teams in India and Mexico.

Essential Skills/Experience:
- Understand and help complete the vision, operating model, and multi-year roadmap for agentic AI and MLOps. This applies across C&O services and aligns with business outcomes, risk posture, and global scale. 
- Team formation and leadership: Build, lead, and develop the agent manager team; set role definitions, hiring plans, career frameworks, and a follow-the-sun support model to ensure global coverage. 
- AI workflow deployment and maintenance: Oversee lifecycle operations for C&O AI agents and workflows spanning all of the phases of the AstraZeneca end-to-end content lifecycle and the underlying content supply chain. 
- Integration across the content supply chain: Ensure seamless orchestration with content platforms, DAM, CRM, and activation channels; implement standard taxonomies, metadata, and handoffs to minimize friction and rework. 
- Partner with relevant collaborators to align ontologies, customer data, knowledge graphs, and governance policies with marketing objectives. Enable efficient and effective retrieval-augmented generation and robust measurement.  
- Operational management: Establish service management practices (backlogs, SLAs/SLOs, runbooks, change management), capacity planning, and vendor/partner oversight; implement FinOps for AI compute and inference costs. 
- Risk, compliance, and responsible AI: Implement guardrails for privacy, consent, factuality, bias, and safety; enforce auditability, human-in-the-loop controls, and adherence to applicable regulations and internal policies. 
- Platform engineering partnership: Co-own requirements for the agent platform (timely assets, tool registries, connectors, feature stores, vector indices, model registries, observability) with Commercial IT, CDH and relevant business unit collaborators.  
- Standards and governance: Define policies for prompt/version management, data lineage, content provenance, and incident response; institute quality gates, evaluation protocols, and rollback procedures. 
- Cross-functional collaboration: Collaborate with Content Excellence, Channel Excellence, Process Excellence, MLR/Legal/Privacy, Commercial IT, and CDH to prioritize use cases, remove blockers, and drive adoption. 
- Performance and return on investment: Set measures and dashboards for efficiency, cycle time, reliability, accuracy, personalization uplift, and cost per output; run experiments and post-implementation reviews to optimize impact.  
- Provide subject matter expertise to the Process Excellence team. Help build playbooks and training programmes. Support integration of AI knowledge into C&O communities of practice. Promote agentic AI literacy and responsible use across C&O, especially in business services teams in India and Mexico.

Desirable Skills/Experience:
- Track record scaling MLOps and agentic AI in regulated or highly governed environments such as healthcare, finance, or life sciences
- Expertise with content supply chain tooling, including DAM, CRM, and activation platforms; experience standardizing taxonomies and metadata
- Hands-on knowledge of RAG patterns, vector databases, feature stores, model registries, and prompt management at scale
- Practical experience with observability for AI systems, evaluation harnesses, QA automation, and rollback procedures
- Familiarity with privacy and marketing regulations and processes (e.g., GDPR/CCPA, consent management, MLR review)
- Proven leadership of follow-the-sun operations and vendor/partner ecosystems across multiple regions, including India and Mexico
- FinOps experience managing AI compute and inference costs, capacity planning, and performance tuning
- Strong collaborator management across technical and non-technical teams; ability to translate business objectives into operational plans
- Exposure to experimentation frameworks, A/B testing, and measurement of personalization uplift and cost per output
- Relevant certifications or contributions in MLOps, cloud platforms, data governance, or responsible AI

When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.

Why AstraZeneca:
Here, your work powers the enterprise and ultimately helps patients—by making our operations faster, more reliable, and smarter. You will join a community that pairs deep technical craft with genuine collaboration, where diverse teams come together to challenge assumptions and take smart risks. We invest in new technology and in our people, giving you the room to experiment with modern AI platforms, learn from a global network, and see your ideas adopted at scale. We value kindness alongside ambition, and we back bold execution with the support needed to do the right thing.

Call to Action:
Ready to build the operational backbone for agentic AI at global scale and see your impact ripple across the enterprise—submit your application today and shape what high-performance AI can deliver!

Date Posted

Closing Date

AstraZeneca embraces diversity and equality of opportunity.  We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills.  We believe that the more inclusive we are, the better our work will be.  We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics.  We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.