Related skills
azure aws sql python google cloud📋 Description
- Translating requirements to ML models for real-world problems.
- Data Science: run ML experiments with ML libraries.
- GenAI: leverage generative AI for innovative solutions.
- Optimisation: optimise ML solutions for performance and scalability.
- Data Engineering: ensure efficient data flow between databases and backends.
- MLOps: automate ML workflows with testing and reproducibility.
🎯 Requirements
- Experience: 4-7 years as a ML Engineer, ideally consulting.
- Python: production-ready backend development.
- Cloud: Google Cloud, AWS, or Azure.
- SQL: strong knowledge of SQL.
- GPUs/Distributed: experience scaling with GPUs or distributed systems.
- ML Integration: expose ML components via web services (e.g., Flask).
🎁 Benefits
- Holiday: 25 days plus bank holidays.
- Health: Private health insurance (Vitality) and Smart Health Services.
- Hybrid Model: a WFH allowance to keep you comfortable.
- Learning & Growth: Udemy access.
- Pension: auto-enrolment with employer contributions.
- Life Insurance: 3x base salary.
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