Tech Lead, Shared AI Services

NEMETSCHEK AG; Remote; USD 130,000 - USD 160,000; Full time

Job summary

A leading technology firm is seeking a skilled leader to build and manage a high-performing engineering team for AI services. The role involves owning the technical architecture and ensuring platform governance across shared components. A strong background in AI/ML platforms, along with 8+ years of engineering experience, is required. The ideal candidate will foster collaboration and align technical decisions with business outcomes, supporting the digital transformation within the industry.

Qualifications

• 8+ years of engineering experience with 3+ years in leadership.
• Proven delivery of AI/ML platforms in production.
• Relevant certifications in cloud architecture or security preferred.

Responsibilities

• Build and develop a high-performing engineering team.
• Own the technical architecture and ensure platform governance.
• Partner with product leaders to prioritize AI use cases.

Skills

Leadership

Systems architecture

Cloud services

AI/ML platforms

Stakeholder communication

Fluent English

German

Education

Bachelor’s or Master’s in Computer Science/Software Engineering

Tools

Kubernetes

CI/CD

Job description

The Nemetschek Group is a pioneer for the digital transformation in the AEC/O industry and focuses on the use of open standards (OPEN BIM). As one of the world's leading groups in this industry, the Nemetschek Group increases the quality in the construction process with its intelligent software solutions and improves the digital workflow of all those involved in the process. As a result, buildings can be planned, built, and operated more efficiently, sustainably and with less impact on resources.

Responsibilities:

• Build, lead, and develop a high-performing engineering team delivering full-stack shared AI services (e.g., AI Assistant backend), including hiring, mentoring, and performance management.
• Own the end-to-end, full stack technical architecture and platform governance for shared components across brands, ensuring scalability, reliability, and interoperability.
• Partner with product and engineering leaders to help prioritize cross-brand AI use cases, maintain a shared backlog, and align delivery timelines and dependencies.
• Deliver platform capabilities such as agent runtime and store, fine-tuning and evaluation pipelines, SDKs/APIs, and semantic translation adapters for cross-brand workflows.
• Establish and enforce engineering best practices for AI services, including code quality, documentation, automated testing, observability, CI/CD, versioning, and release management.
• Ensure security, privacy, and compliance across services, including model transparency and monitoring, data protection, and incident response processes.
• Define and track platform KPIs (availability, latency, cost, quality metrics), implement performance and quality evaluation, and ensure continuous platform upkeep.
• Operate a cross-brand contribution model: review, generalize, and merge brand contributions into the common core through rigorous technical reviews and quality gates.
• Align technical decisions with business outcomes; communicate impact, capacity needs, and investment trade-offs; support budgeting and resource allocation for shared services.

Qualifications:

• Educational Background: Bachelor’s or Master’s in Computer Science/Software Engineering or related; equivalent practical experience in large-scale platform engineering; relevant certifications (cloud architect, security, MLOps) are a plus.
• Professional Knowledge and Experiences: 8+ years engineering with 3+ years leading platform/shared services; proven delivery of AI/ML platforms in production; cloud/Kubernetes/service mesh/observability; AI platform components (orchestration, vector stores, RAG, fine‑tuning, eval harnesses); strong architecture across backend/data/APIs; security/privacy/incident response; SLO-driven operations/on-call/postmortems; cross-brand platform and contribution model management.
• Other Skills and Competencies: Leadership and coaching; systems architecture and governance; product-minded delivery; SRE, observability with SLOs/SLIs, and cost/performance stewardship; security/privacy and model governance; stakeholder communication and clear documentation; cross-brand collaboration; languages: fluent English, German is a plus.