Machine Learning Engineer - Prototyping
Posted Today by Mpower Plus UK Ltd
Negotiable
Undetermined
Hybrid
London (Hybrid 3 days), UK
Summary: The Machine Learning Engineer - Prototyping role focuses on leveraging Python for machine learning prototyping, data processing, and automation. The position requires building end-to-end Proof of Concepts (PoCs) and translating business ideas into technical solutions efficiently. Candidates should possess strong data engineering skills and the ability to deliver prototypes quickly while maintaining a practical mindset towards business outcomes.
Key Responsibilities:
- Develop and prototype machine learning solutions using Python.
- Build end-to-end Proof of Concepts (PoCs) and rapid prototypes.
- Translate ambiguous business ideas into working technical solutions.
- Implement data engineering tasks including APIs, data pipelines, and SQL.
- Develop lightweight applications such as APIs, dashboards, and notebooks.
- Deliver PoCs within short timelines and scale them into prototypes.
- Work independently in fast-paced environments and iterate based on feedback.
- Communicate findings and feasibility outcomes effectively.
Key Skills:
- Strong hands-on experience with Python for ML prototyping.
- Data engineering skills including APIs, data pipelines, and SQL.
- Good understanding of machine learning and statistical fundamentals.
- Experience in developing lightweight applications.
- Strong problem-solving and analytical thinking skills.
- Ability to work independently and adapt quickly.
- Strong communication skills for presenting findings.
Salary (Rate): undetermined
City: London
Country: UK
Working Arrangements: hybrid
IR35 Status: undetermined
Seniority Level: undetermined
Industry: IT
Strong hands-on experience with Python for ML prototyping, data processing, and automation.
Experience building end-to-end Proof of Concepts (PoCs) and rapid prototypes.
Ability to translate ambiguous business ideas into working technical solutions quickly.
Strong data engineering skills including APIs, data pipelines, SQL, and cloud-based data handling.
Experience developing lightweight applications such as APIs, dashboards, notebooks, or simple frontends.
Good understanding of machine learning and statistical/mathematical fundamentals.
Experience working independently in fast-paced and rapidly changing environments.
Strong problem-solving, analytical thinking, and experimentation mindset.
Ability to iterate quickly based on stakeholder feedback.
Strong communication skills with the ability to clearly present findings and feasibility outcomes.
Experience delivering PoCs within short timelines (2-3 days) and scaling them into prototypes within 2-3 weeks.
Practical mindset focused on business outcomes rather than perfect production-grade code.
Experience working across the full life cycle: data ingestion - modelling - prototype delivery.