AI Tech Lead

AI Tech Lead

Key Responsibilities

1. Build & Lead the AI Engineering Capability

Establish the engineering culture, development practices, and team norms in a new hub.

Mentor and coach individuals – be open for moving into an engineering manager role over time.

Support hiring, onboarding, and continuous development of engineers.

1. Technical Leadership and Implementation Across a Broad Range of AI Use Cases

Work with the AI Center of Excellence Use case funnel to understand, shape, and assess emerging

AI use cases across the org.

Implement progressive AI solutions for various business domains in agile, and cross-functional

teams.

Rapidly prototype solutions to validate feasibility and value in a scalable manner.

Provide architectural guidance for AI services, agents, automations, and ML-based features.

Industrialize and maintain proven-value use cases.

Co-develop use cases with domain SW teams in temporary cross-functional team set-ups.

Implement solutions that use AI and ML to deeply understand the complexity of parts to optimize

our manufacturing processes and fulfilment within our manufacturing partner network (Computer

vision, LLMs, more classical DL, Geo Analysis for DFM and inference processing pipelines)

1. Contribute to the AI Development Platform & Environment

Set up foundational environments for (Gen)AI experimentation, model development, evaluation,

and deployment.

Collaborate with global platform counterparts to implement MLOps, CI/CD, data pipelines, and

observability.

Advocate for reusable components, shared libraries, and internal tooling to accelerate use-case

delivery.

Key Competencies

Technical

• 5+ years of hands-on AI/ML engineering experience, including modern deep learning, LLMs, transformers, vector stores, retrieval techniques, and model evaluation.
• Strong Python engineering skills and experience with core AI frameworks such as PyTorch,
• TensorFlow/Keras, and GenAI stacks like LangChain, LangGraph, LlamaIndex, and OpenAI/Azure/AWS SDKs.
• Proven ability to evaluate build vs buy vs adapt decisions based on cost, effort, risk, and long-term maintainability.
• Cloud-native AI development, preferably on AWS (SageMaker, Bedrock), with experience building scalable, secure, and compliant production systems.
• Demonstrated experience with production-grade GenAI systems, including RAG pipelines, hybrid search, embedding strategies, and agentic workflows.
• Strong MLOps background: CI/CD, model deployment, monitoring/observability, IaC, and workflow orchestration using tools like Prefect and Weights & Biases, or equivalents.
• Experience with vector databases, scalable retrieval architectures, guardrailing LLMs and prompt engineering.
• Familiarity with modern AI system design, including API serving, containerization (Docker), and distributed compute.

Leadership & Collaboration

• Experience working in Agile environments, including running or contributing to Scrum
• ceremonies, breaking down work into increments, and collaborating closely with TPM and EM in iterative delivery.
• Ability to lead a team through ambiguity and early-stage environments.
• Proactive style and strong ability for systematic problem solving.
• Excellent communication capabilities across global departments.
• Critical thinking in uncertain or rapidly evolving contexts.
• Mindset
• Builder mentality: pragmatic, resourceful, iterative.
• Curiosity and willingness to experiment while ensuring quality and security.
• Passion for developing talent and creating a high-performing team culture.