Senior ML/AI Engineer
New
S
SweedCannabis Retail Technology
Remote InternationalFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLArtificial IntelligenceMachine LearningMLOps
Requirements
- 5+ years of production engineering experience in machine learning, AI engineering, backend engineering
- Strong Python and SQL skills
- Experience with LLMs, RAG systems, AI agents, or orchestration frameworks such as LangGraph or PydanticAI
- Hands-on experience building and shipping production systems
- Experience with modern AI/LLM ecosystems, agentic frameworks
- Understanding of ML/AI system design, experimentation, and production deployment practices
- Experience building APIs or backend services
- Experience with data processing and analytics workflows
- Familiarity with MLOps concepts, monitoring, CI/CD, logging, and production reliability
- Ability to work in ambiguous environments and independently drive technical initiatives
- Strong communication skills and comfort working cross-functionally across multiple teams
Responsibilities
- Build and productionize AI-powered product features across eCommerce, marketing, analytics, and customer workflows
- Design and implement agentic AI systems and LLM-powered workflows
- Develop, train, evaluate, and deploy machine learning models into production environments
- Work across the full ML lifecycle: experimentation, feature engineering, model evaluation, deployment, monitoring, and iteration
- Collaborate closely with product managers, backend engineers, analysts, and DevOps teams to bring AI systems into production
- Design scalable APIs and inference services for AI-driven functionality
- Improve and evolve our ML infrastructure, tooling, and AI-native development workflows
- Rapidly prototype ideas, validate hypotheses, and iterate based on real-world feedback and business impact
- Contribute to experimentation frameworks, A/B testing approaches, and production monitoring practices
- Stay current with the latest developments in LLMs, agentic systems, generative AI, and applied machine learning
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