Technical Architect - AI/ML Ops

Technical Architect - AI/Client Ops

Auburn Hills, MI (Remote)

Client: Volkswagen

Overall 10+ years required. Maximum number of submissions: 2

Job Requirements

The AI/MLOps Engineer (AMOE) is an applied solutions expert who combines working knowledge of software engineering, machine learning, and DevOps best practices. As part of a small team of dedicated AI problem solvers, an AMOE draws upon their practical skills to build CI/CD pipelines, deploy applications, promote security by design, and generally automate all the things. Our team creates production Client/AI applications spanning multiple business domains, from logistics and operations to sales and business optimization. Our work often involves collaboration with vendors, business stakeholders, etc., therefore communication skills and the ability to capture ideas in diagrams and documentation are equally important. To help everyone sleep soundly at night, we seek teammates who write code with an eye for maintainability and testability. We like integration tests, CI/CD, observability, and twelve-factor design.

The Ideal Candidate:

• Enjoys learning across the tech stack, from new developments in DevSecOps and automation to machine learning and artificial intelligence • Takes pride in their work and derives great satisfaction from building reliable and maintainable infrastructure to support our team • Isn't afraid to voice opinions, propose solutions, and receive constructive criticism in a collaborative design process • Is a social coder who likes sharing and receiving code reviews, and values pair programming where appropriate • Thrives in a remote-first work environment and uses remote collaboration tools effectively

Preferred Qualifications:

• Fluent in Python. Fluent candidates can comfortably: read and write Python code; write method and class docstrings; manage package dependencies in a principled way. • Comfortable with git. We use GitHub extensively to develop and deploy our code; candidates should be comfortable writing quality PRs, utilizing GitOps, etc. • Strong command line skills in *nix environments. Our code is built in and deployed to Unix-like environments; candidates should feel at home on the command line. • Excellent understanding of networking and security. Our work often extends across multiple networks and environments and deals with sensitive data. • Understands containers and orchestration. The ideal candidate will be comfortable containerizing applications, working with Kubernetes, and running systems in production. • Experienced in IaC and cloud deployment. We deploy applications to private and public cloud environments using cloud-native tooling and IaC principles (Terraform, Argo CD, Istio, etc.). • Familiar with Client/AI concepts and practices. Candidates should be comfortable with concepts such as model development, training, and monitoring.

Bonus Qualifications:

• Knowledge of standard Client/AI libraries. For example, Pandas, scikit-learn, TensorFlow/PyTorch/JAX, Kubeflow, etc. • Fluency in other programming languages. Much of the cloud-native ecosystem is built in Go; many of our applications include JavaScript-based components. • Familiarity with software architecture. We are often tasked with building new systems and designing infrastructure for scale. • Proficiency in SQL. Knowledge of SQL is occasionally required for data ingestion and analysis. • Proven engineering background. A technical degree in a field such as Computer Science or Data Analytics, and/or equivalent work experience. • Relevant certifications. Technical certifications, particularly ones related to DevOps (CKA, CKS, RHCSA, etc.).