AI Engineer (AI Strategy & Enablement) (m/f/d)

Key Responsibilities

• Lead the strategy, design, and delivery of AI-driven solutions, spanning LLMs, ML models, and intelligent workflows.
• Architect and implement scalable AI systems that integrate smoothly with enterprise-grade infrastructures and development practices.
• Collaborate closely with engineering, product, and leadership teams to identify where AI can deliver the most value, and help turn those opportunities into real solutions.
• Develop and maintain internal tooling, frameworks, and guidelines to enable other teams to work effectively with AI and ML technologies.
• Guide the adoption of GenAI capabilities, including prompt engineering, RAG pipelines, and agent-based architectures, always with a focus on long-term maintainability.
• Ensure systems are observable, testable, performant, and aligned with data privacy, security, and compliance needs.
• Stay actively informed on the evolving AI/ML ecosystem (GenAI, MLOps, vector search, model serving) and evaluate new tools and practices for enterprise readiness.
• Promote a culture of learning, experimentation, and thoughtful adoption of AI technologies across teams.

Key Requirements

• 4+ years of experience in software or ML engineering, with a strong foundation in backend architecture and distributed systems.
• Proven track record designing and delivering AI-powered systems in production, preferably in enterprise environments.
• Proficient in Python (or Node.js), with deep experience building robust, maintainable, and scalable services.
• Strong understanding of LLMs, embeddings, prompt engineering, RAG patterns, and GenAI tooling (e.g., LangChain, Transformers, Hugging Face, OpenAI APIs).
• Comfortable building for real-world complexity: multi-tenant setups, observability, performance, cost tracking, and governance.
• Familiarity with modern infrastructure tooling: containerisation (Docker), orchestration (e.g., Airflow, Temporal), cloud services (AWS, Azure, GCP).
• Experience driving cross-functional initiatives and mentoring technical teams on AI capabilities.
• Fluent in English and an excellent communicator, able to collaborate effectively across disciplines.

Nice-to-Have

• Experience with model serving and inference frameworks (e.g., vLLM, TensorRT-LLM, LiteLLM).
• Familiarity with vector databases (e.g., Qdrant, Weaviate, Pinecone) and semantic search design.
• Exposure to MLOps or AIOps practices, including monitoring, retraining, and lifecycle management.
• Background in data science or ML beyond GenAI use cases—e.g., time-series, anomaly detection, recommendation systems.
• Contributions to open-source tools or internal enablement platforms.

Perks and Benefits