Lead ML Platform Engineer

Local candidates preferred.

Job Duties and Responsibilities:
The Lead ML Platform Engineer provides architecture and hands-on engineering leadership for the Cortex Predictive AI Platform across cloud and on-premises environments. This role will establish and implement reusable, secure, scalable standards that enable data scientists, ML engineers, and application teams to build, validate, deploy, monitor, and operate predictive models efficiently and reliably.
The successful candidate will lead technical design and engineering decisions across the ML platform lifecycle, including governed data and feature access, model development environments, training and validation workflows, model delivery pipelines, real-time and batch inference, observability, reliability, and operational readiness. This role will also mentor engineering teams and transfer knowledge to support sustainable platform operations and adoption.

Key Responsibilities
Define and lead the target architecture for predictive AI and ML platform capabilities spanning public cloud and on-premises environments.
Design, build, and operate reusable platform services supporting the end-to-end ML lifecycle: governed data and features, model development, training, validation, deployment, inference, monitoring, and operations.
Establish scalable reference architectures, engineering standards, reusable templates, and implementation patterns for ML workloads across the Cortex portfolio.
Lead platform engineering for Google Cloud Platform and multi-cloud environments, including secure connectivity, identity, network controls, compute, storage, and managed AI/ML services where applicable.
Design and operate Kubernetes-based ML platforms using GKE, OpenShift, and associated container, workload orchestration, and resource-management capabilities.
Implement and improve MLOps capabilities for experiment tracking, model packaging, validation, approval gates, model registry integration, deployment automation, rollback, and lifecycle management.
Build CI/CD pipelines and infrastructure automation for platform services, ML workflows, model delivery, and environment provisioning.
Enable model migration from legacy environments into standardized Cortex platform patterns, minimizing delivery risk and operational disruption.
Engineer production-grade real-time and batch inference capabilities, including API-based serving, scalable runtime patterns, resiliency, performance, and operational support.
Partner with data engineering, data governance, security, privacy, risk, model validation, and application teams to ensure data protection and control requirements are embedded into platform design.
Implement platform observability, including logs, metrics, traces, dashboards, alerts, service-level indicators, service-level objectives, and operational runbooks.
Drive reliability engineering practices for ML platform services, including capacity planning, high availability, disaster recovery, incident management, root-cause analysis, and continuous improvement.
Ensure platform designs meet enterprise security requirements for authentication, authorization, secrets management, encryption, data access, auditability, and environment isolation.
Provide technical leadership, architecture reviews, code reviews, design guidance, and mentoring to ML platform engineers and adjacent delivery teams.
Produce clear technical documentation, reference implementations, operational procedures, and knowledge-transfer materials to enable self-service adoption and long-term support.

Required Qualifications
8+ years of experience in platform engineering, cloud engineering, infrastructure engineering, SRE, MLOps, or related technical roles.
4+ years of experience designing, building, or operating enterprise AI/ML or data platforms.
Demonstrated experience leading architecture and engineering delivery for complex, production-grade cloud and/or on-premises platforms.
Strong hands-on experience with Google Cloud Platform and working knowledge of multi-cloud or hybrid-cloud architecture.
Experience with Kubernetes-based platforms, including GKE and OpenShift, in production environments.
Strong experience implementing MLOps capabilities, model lifecycle workflows, or ML platform services.
Proficiency in Python for platform automation, integration, operational tooling, or ML workflow development.
Experience with CI/CD, Git-based development, automated testing, deployment automation, and infrastructure-as-code practices.
Strong understanding of enterprise security, data protection, identity and access management, secrets management, encryption, audit logging, and secure software delivery.
Experience implementing observability, monitoring, alerting, dashboards, SLOs, incident response, and operational runbooks.
Experience mentoring engineers and communicating technical architecture decisions to engineering, product, security, data, and executive stakeholders.

Required Skills / Knowledge
Enterprise ML platform architecture and end-to-end predictive model lifecycle management.
Google Cloud Platform, hybrid cloud, multi-cloud, on-premises platform, networking, identity, and security concepts.
Kubernetes, GKE, OpenShift, containers, workload orchestration, and scalable compute platforms.
MLOps, model development environments, model registries, validation workflows, model deployment, and model monitoring.
Python, CI/CD, Git, automated testing, infrastructure automation, and API-based integration.
Real-time and batch inference architecture, model-serving patterns, performance optimization, and operational support.
Data protection, governance, access controls, encryption, auditability, and regulated-platform design.
Observability, telemetry, dashboards, alerting, SLI/SLO design, reliability engineering, and production troubleshooting.
Technical leadership, reusable pattern development, engineering documentation, and knowledge transfer.

Preferred Qualifications
Experience with Vertex AI or comparable cloud ML platform services.
Experience designing or operating on-premises AI/ML platforms, private cloud, or hybrid ML workloads.
Experience with feature stores, model registries, experiment tracking, data lineage, model governance, or model risk-management processes.
Experience supporting model migration, platform modernization, or transition from legacy data science and ML environments.
Experience with real-time, low-latency model-serving systems and event-driven inference architectures.
Experience with Terraform, Helm, Argo CD, Jenkins, GitHub Actions, GitLab CI, or similar automation and deployment tooling.
Experience in banking, financial services, healthcare, insurance, or another regulated enterprise environment.
Experience establishing self-service platform capabilities for data scientists, ML engineers, and application teams.

Expected Outcomes
A secure, scalable, and reusable Cortex ML platform architecture spanning public cloud and on-premises environments.
Standardized MLOps, CI/CD, and model-delivery patterns that reduce time to train, validate, deploy, and operate predictive models.
Reliable platform capabilities for governed data and features, model migration, batch and real-time inference, and production operations.
Improved observability, resiliency, service-level management, and operational readiness for ML platform services and models.
Reusable engineering standards, reference implementations, documentation, and knowledge-transfer assets that enable self-service adoption and sustainable platform support.

Thanks & Regards

Vinay K ||