Director AI Platform Engineering

Academic Key; Germany; Hybrid; EUR 180,000 - 240,000; Full time

Job summary

Academic Key is building a new AI Engineering & Platform capability in Germany to support AI-powered media, localization, automation, and workflow services. The Director will lead a hybrid team, own cloud-native training and inference platforms, and drive production AI services across model deployment and operations.

You will recruit and grow the team, establish MLOps practices, optimize GPU cost, implement monitoring and CI/CD, and collaborate with stakeholders in the U.S.

Qualifications

• 10+ years in software/ML engineering, incl. 4+ years in technical leadership.
• Hands-on experience with model training/fine-tuning and production deployment.
• Proven ability to design/operates production ML infra on major cloud providers (AWS/GCP/Azure).

Responsibilities

• Build, lead, and mentor a distributed AI engineering team across Germany and other locations.
• Own cloud-native training/inference platform for NLP tech in production.
• Drive architecture, deployment, observability, and cost management for AI services.

Skills

Leadership

Cloud & ML infra

Model training & deployment

Kubernetes & Docker

Terraform

Tools

Kubernetes

Docker

Terraform

Slurm/HPC

Job description

Director AI Platform Engineering

Our client is building a new AI Engineering & Platform team in Germany to support the next generation of AI-powered media, localization, automation, and workflow capabilities. The Director, AI Engineering & Platform will lead this team and will be responsible for building, operating, and evolving the technical foundation for production AI services across our client. This is a senior hands-on leadership role for someone who can build and run a high-performing engineering team, deliver complex AI platform and integration projects, and provide technical direction across cloud infrastructure, model deployment, AI services, agentic workflows, and production operations. The role will lead a hybrid engineering team based primarily in Germany while also managing and collaborating with engineers in other our client locations, including the United States. In addition to leading delivery and operations, this person will continuously evaluate emerging AI technologies, model providers, open-source frameworks, agentic workflow patterns, and industry capabilities. They will be expected to make clear recommendations on where our client should build, buy, partner, or integrate in order to improve speed, quality, cost, automation, and product differentiation. RESPONSIBILITIES: Build, lead, mentor, and develop a distributed AI engineering team across Germany and other our client locations. Experience recruiting, hiring, and standing up the full AI/ML engineering organization from scratch: applied ML engineers, MLOps/platform engineers, data engineers, integration engineers, etc. Lead development, hosting and ongoing improvement of our client’s cloud-native training and inference platform (compute, storage, orchestration, model serving, monitoring, cost management) for our client’s NLP technology for MT, ASR, and TTS in production. Own technical delivery for our client’s AI platform, model-serving, workflow automation, and applied AI engineering initiatives. Lead architecture,execution, deployment and SLA performance for scalable AI services, including inference platforms, training environments, deployment pipelines, observability, and operational support. Own infrastructure cost efficiency (GPU utilization, training vs. inference cost trade-offs, autoscaling) as a first-class, ongoing responsibility rather than a one-time setup task. Evaluate internal and external AI technologies on an ongoing basis, including commercial models, open-source models, agentic frameworks, orchestration tools, and vendor platforms. Recommend when our client should build, buy, partner, or integrate with third-party AI capabilities. Guide teams responsible for cloud infrastructure, DevOps, AI platform operations, applied AI engineering, and model integration. Establish engineering standards, operating rhythms, delivery practices, documentation expectations, and technical review processes. Establish and drive MLOps practices: continuous improvement across AI engineering practices, reproducible training pipelines, model versioning and registry, automation, CI/CD, monitoring, automated quality evaluation, testing, and release management. Support adoption of AI capabilities across our client applications and workflows through reusable platforms, services, APIs, and technical patterns. Create clarity around priorities, risks, technical tradeoffs, delivery timelines, and resourcing needs. Ensure the team can operate production AI systems independently through strong documentation, runbooks, monitoring, incident response, and knowledge sharing. Partner with U.S. and European stakeholders across engineering, product, operations, localization, media workflows, and business leadership. Partner closely with the leaders of our client’s localization business lines to understand where current MT/ASR/TTS quality falls short and translate that into a prioritized technical roadmap. Ensure AI systems are reliable, secure, maintainable, cost-conscious, and aligned with production business needs. Represent AI/ML technology capability and constraints to executive leadership; contribute to the company’s overall technology and product strategy. QUALIFICATIONS: [10+] years in software/ML engineering, including [4+] years in a technical leadership role owning both people management and architecture/system-design decisions. Direct, hands-on experience with model training/fine-tuning, evaluation methodology, and production deployment (not solely API-consumer experience). Proven experience designing and operating production ML infrastructure at scale: training pipelines, model serving/inference, GPU resource management, and MLOps tooling, on at least one major cloud provider (AWS, GCP, or Azure). Experience in distributed training, Slurm/HPC, Kubernetes, Docker, Terraform, or similar infrastructure technologies. Track record of building a technical team from scratch to a functioning, multi-workstream organization, including recruiting, leveling, and distributed team design acr

Academic Key will never request login credentials or application fees for this role.