Sr. Engineer, AI Platform

Abbott•Madison, WI

1d•Onsite

About The Position

The Sr AI Platform Infrastructure Engineer is a hands-on technical leadership role responsible for the architecture, reliability, scalability, security, and cost of infrastructure underlying Abbott’s Cancer Diagnostics AI/ML platform. The engineer partners with the AI Platform Software and Safety leads to develop and operate infrastructure supporting large language models, predictive models, agentic systems, and traditional software applications across the MLOps lifecycle. The position is based in Madison, Wisconsin. The role owns the cloud and Kubernetes foundation, infrastructure-as-code and GitOps, CI/CD and deployment tooling, production model-serving and GPU infrastructure, workflow orchestration and event-streaming systems, observability and disaster recovery, and infrastructure security and governance. It also involves contributing production software, mentoring engineers, representing the infrastructure pillar in design reviews and technology planning, and upholding Abbott’s mission and values through accountability, innovation, integrity, quality, teamwork, and an inclusion mindset.

Requirements

Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
Demonstrated expertise designing and deploying cutting-edge AI solutions that drive measurable business impact.
Demonstrated understanding of ethical considerations in AI systems.
At least 3 years of project leadership experience, including Agile project management and Scaled Agile Frameworks (SAFe).
Advanced ability working with machine learning frameworks, programming languages such as Python, and cloud platforms.
Strong analytical and problem-solving skills, with expertise in AI/ML techniques.

Nice To Haves

Production experience with Python, Go, and/or Rust.
Experience leading platform transformations and/or building large-scale AI infrastructure.
Multi-cloud experience or experience migrating significant workloads between cloud providers.
Experience with Kubernetes-native serverless and event-driven autoscaling, such as Knative or KEDA, service mesh, API gateway, and zero-trust network patterns.
Cost ownership of a GPU fleet or comparable FinOps accountability for shared compute.
Experience operating within enterprise guardrails where other teams own network, identity, and change management.
Knowledge of compliance frameworks such as HIPAA, FDA, and GxP.
Experience guiding cross-functional engineering groups or mentoring senior-level engineers.

Responsibilities

Design and own the platform’s cloud and Kubernetes foundation, including account and network topology, VPC and subnet design, private connectivity, ingress and egress controls, DNS and certificates, and production-cluster provisioning, upgrades, migrations, multi-tenant isolation, and security.
Lead platform expansion across clusters and regions, including topology, identity federation, state and data placement, traffic management, and failure domains; evaluate multi-cloud when business needs warrant it.
Own infrastructure-as-code and GitOps, including module design, state management, environment promotion, drift control, and testing and rollback of infrastructure changes.
Build and operate CI/CD and deployment tooling for platform software releases in partnership with the AI Platform Software lead.
Architect, build, and operate production model-serving and GPU infrastructure for large language and predictive models, including scheduling, multi-tenancy, autoscaling, low-latency serving, inference optimization, capacity planning, and shared-compute cost attribution.
Partner with the AI Platform Software and Safety leads on model-lifecycle infrastructure, including reproducible data, feature and training pipelines with dataset-to-model lineage, promotion environments, rollout traffic shifting and rollback, and automated evaluation and promotion gates.
Architect, build, and operate workflow orchestration and event-streaming infrastructure, including workflow and state-machine design, event-driven triggers and scheduling, retries and idempotency, topic and partition design, schema management, delivery semantics, and cross-region replication.
Own the observability stack and service level objectives for infrastructure services, with proactive platform detection.
Lead disaster-recovery strategy, balancing cost against recovery time and recovery point objectives.
Escalate and lead response to complex infrastructure incidents and conduct post-incident analysis to drive systemic reliability improvements.
Own infrastructure security and governance, including least-privilege identity, secrets management, network policy, workload isolation, and auditability; partner with the safety lead to implement governance controls as automated checks.
Design, develop, test, review, and deploy production software as a hands-on contributor.
Mentor engineers and represent the infrastructure pillar in design reviews and long-term technology planning.
Uphold company mission and values through accountability, innovation, integrity, quality, and teamwork; maintain regular and reliable attendance; and model an inclusion mindset.