Key skills
CloudPythonAIMachine LearningMLLLMRAGLangChainAgenticMLOpsSaaSCommunicationRemote Work
About this role
Role Overview
- Set the technical direction for AI/ML infrastructure: defining standards, reviewing architectures, and driving decisions on tooling and frameworks.
- Build and own the production infrastructure that takes ML and LLM-powered solutions from prototype to live product, ensuring reliability, scalability, and fast iteration cycles.
- Lead the development of agentic AI systems for domain-specific business problems, including systems of agents, fine-tuning, RAG pipelines, and full lifecycle ownership.
- Define model evaluation strategies, performance monitoring, and key indicators to measure the real-world impact of deployed solutions.
- Ensure AI systems are safe, observable, and maintainable by including guardrails, drift detection, and responsible deployment practices.
- Collaborate with Product and Engineering to translate complex business problems into well-scoped AI projects, and communicate results clearly to non-technical stakeholders.
- Mentor and support other engineers on the Data team on productionization of ML and AI.
Requirements
- 7+ years of experience as a Machine Learning Engineer or Applied AI Engineer, with a meaningful portion in production environments.
- Strong Python skills and experience building robust, production-grade ML systems.
- Hands-on experience with LLMs and agentic AI frameworks (e.g. LangChain, PydanticAI, or similar), including RAG pipelines and tool-calling agents (fine-tuning experience a plus)
- Experience with MLOps practices: model registry, monitoring, and drift detection.
- Proven ability to take ML solutions from prototype to production, including evaluation frameworks, versioning, and iteration.
- Comfortable working in a cloud-native environment and collaborating with SRE on deployment and infrastructure concerns.
- Ability to drive architectural decisions and define ML best practices across a team.
- Strong communication skills: comfortable translating technical findings into business impact for diverse stakeholders.
- Experience with multi-tenant ML model deployment in a SaaS environment a plus.
Tech Stack
- Cloud
- Python
Benefits
- Attractive compensation package
- Hybrid model with remote days to support balance and flexibility.
- Enjoy up to 15 days of remote work from abroad each year.
- helloCSE: discount platform for fitness, shopping, culture, cinema, live events
- Multicultural colleagues, after-work events, team-building & more.