Platform Engineering Manager

Sia is a next-generation, global management consulting group. Founded in 1999, we were born digital. Today our strategy and management capabilities are augmented by data science, enhanced by creativity, and driven by responsibility. We're optimists for change and we help clients initiate, navigate, and benefit from transformation. We believe optimism is a force multiplier, helping clients mitigate downside and maximize opportunity.

With expertise across a broad range of sectors and services, our consultants serve clients worldwide. Our expertise delivers results. Our optimism transforms outcomes.

Job Description

Sia Partners is looking for a Platform Engineering Manager to support the design and delivery of next-generation AI and Generative AI platforms within Sia's AI Factory. This role is pivotal in bridging high-level product vision with robust, cloud-native engineering execution.
As a Platform Engineering Manager, you will be responsible for building and evolving internal development and MLOps platforms that improve automation, scalability, reliability, and developer productivity. You will operate as a player-coach, combining hands-on technical leadership with people management and strategic ownership.
This is a product-focused role, working closely with product managers, data scientists, ML engineers, application engineers, and security teams to deliver platform capabilities that directly support AI, data, and software workloads

Key Responsibilities

Leadership of Platform / Dev Ops / SRE engineering teams, ensuring delivery excellence and strong engineering culture

Ownership of internal platform products, working closely with product, application, and data engineering teams to deliver scalable, reliable, and secure platform capabilities.

Support to Data Scientists, ML Engineers, Data Engineers, and Software Engineers by providing reliable, scalable, and easy-to-use platform services

Definition and execution of the platform engineering strategy and roadmap, aligned with business and delivery objectives

Development and operation of internal developer platforms enabling automation, self-service, and scalability

Support to Data Scientists, Data Engineers, and Software Engineers by providing reliable, secure, and scalable platforms

Cloud services: architecture and operations across AWS, Azure, and GCP, including compute, storage, networking, access management, cost monitoring, and cost optimization

Infrastructure as Code: design, standardization, and governance using Terraform

Containers: containerization and orchestration of applications using Docker and Kubernetes, including Kubernetes platform ownership

CI/CD: definition and standardization of continuous integration and deployment pipelines

Observability & reliability: monitoring, logging, alerting, and application of SRE principles to ensure availability, performance, and resilience

Contribution to technological, architectural, and governance decisions to address the challenges of scaling AI and data platforms

Collaboration with product, application, data, and security teams to gather requirements and deliver platform capabilities

Planning and management of platform initiatives, including timelines, resourcing, and budget oversight

Mentoring engineers and fostering knowledge sharing and continuous improvement.

Qualifications

Experience:

8+ years of experience in the data/software space, with at least 3+ years in a formal people management or technical leadership role leading Data Science or ML teams.

Strong hands-on experience with cloud platforms (AWS, Azure, and/or GCP)

Strong expertise in Infrastructure as Code, preferably Terraform

Solid experience with Docker and Kubernetes

Experience designing and operating CI/CD pipelines

Proficiency in Python scripting and strong Linux fundamentals

Good understanding of SRE principles, reliability, scalability, and security best practices

AI-Native Engineering Leadership:
Experience managing teams that utilize Cursor, Git Hub Copilot, or Claude Code as a core part of their daily workflow.

Strong communication, leadership, and stakeholder management skills

Fluency in English, written and spoken.

Additional Information

What Success Looks Like

Platform Ownership:
Treats platform capabilities as long-lived products, not one-off infra projects.

Enablement Mindset:
Measures success by how effectively product teams ship using the platform.

Strong Technical Judgment:
Understands platform trade-offs across scalability, reliability, security, and cost.

Production Readiness:
Ensures platforms are production-grade (deployment, monitoring, rollback, lifecycle).

Reliability & Operations:
Owns uptime, incident response, post-mortems, and continuous improvement.

Developer

Experience:

Reduces friction in CI/CD, environments, tooling, and self-service workflows.

Cost & Efficiency Awareness:
Balances performance with cloud and compute cost optimization

Roadmap & Prioritization:
Aligns platform roadmap with product needs and AI Factory growth.

Team Leadership:
Builds and…