Senior Architect - Cloud Edge AI

Aptiv

About The Position

We are looking for a Senior Architect to serve as a senior, hands-on technical leader for our cloud-native and Edge AI platforms. Reporting to the Director of Engineering, Edge AI, this role focuses on designing, building, and operating the core services that enable AI models and applications to be deployed, managed, and run across distributed edge environments. Our Edge AI platforms sit at the intersection of cloud-native services and embedded intelligence. We build systems that support the full lifecycle of edge workloads—from development and deployment to monitoring, operation, and evolution—enabling teams to deliver intelligent applications reliably and at scale across real-world, distributed environments. This is a 60–70% hands-on role, ideal for a senior technologist who enjoys working close to the code while shaping architecture, standards, and engineering practices across teams.

Requirements

10+ years of software engineering experience, with significant hands-on experience building cloud-scale systems.
Strong expertise in cloud-native architectures, distributed systems, and service-oriented design.
Hands-on experience with Kubernetes, CI/CD, infrastructure as code (e.g., Terraform), and operating production services.
Proven ability to diagnose, debug, and resolve issues in large-scale, live production environments.
Strong grounding in DevSecOps and secure-by-design engineering practices.
Comfortable operating in ambiguous, evolving problem spaces.
Clear communicator who can influence through technical clarity and execution.
Bias toward ownership, quality, and continuous improvement.

Nice To Haves

Familiarity with ML / AI concepts, including model deployment pipelines, inferencing, and working with sensor or streaming data.
Understanding of challenges related to deploying and operating software in edge or resource-constrained environments.

Responsibilities

Design, build, and operate cloud-scale, cloud-native services and platforms.
Act as a technical authority across teams, influencing architecture and engineering standards.
Lead by example through hands-on coding, design reviews, and deep technical problem-solving.
Drive implementation of scalable APIs, services, and supporting infrastructure.
Architect and implement distributed systems using Kubernetes, containerized services, and modern cloud-native patterns.
Build and evolve CI/CD pipelines, infrastructure automation, and deployment workflows using tools such as Terraform.
Ensure platforms are observable, operable, and debuggable at scale, including the ability to run, monitor, and debug production cloud services.
Embed security, reliability, and compliance into system design and development practices.
Define and reinforce best practices for testing, release management, monitoring, and incident response.
Continuously improve system resilience, performance, and cost efficiency.
Support the ML / AI model development lifecycle, including integration of ML pipelines, model packaging, deployment, and inference workflows.
Collaborate with ML and edge teams to ensure cloud platforms effectively support AI workloads deployed to edge devices.
Apply systems thinking to bridge cloud services with edge runtime constraints and operational realities.
Operate effectively in a fast-paced environment where rapid iteration and pivoting based on customer input is the norm.
Work closely with product and customer-facing teams to translate evolving requirements into robust technical solutions.
Use GenAI tools as a coding and design partner to accelerate development and improve engineering productivity.