ML Engineer

Summary: The role of ML Engineer involves developing and deploying machine learning models in a hybrid work environment in London. The position requires a strong background in Python and experience with various ML frameworks and deployment techniques. The contract duration is for 9 months, with a competitive day rate. Candidates should possess relevant experience and skills in model interpretability and data preprocessing.

Key Responsibilities:

• Develop and deploy machine learning models in production-ready environments.
• Pre-process and feature engineer data, including cleaning and transformation.
• Utilize frameworks such as TensorFlow, PyTorch, or Keras for building complex ML models.
• Implement model interpretability and explainability techniques.

Key Skills:

• 3-4 years of relevant experience in machine learning.
• Proficiency in Python and familiarity with popular libraries.
• Knowledge of containerization and orchestration tools (e.g., Docker, Kubernetes).
• Experience with model serving platforms.
• Familiarity with techniques for interpreting and explaining model results (e.g., SHAP, LIME).

Salary (Rate): £700 daily

City: London

Country: United Kingdom

Working Arrangements: hybrid

IR35 Status: inside IR35

Seniority Level: Mid-Level

Industry: IT

Detailed Description From Employer:

Role: ML Engineer

Location: London (Hybrid)

Duration: 9 Months

Day rate: £600 - £700 Inside IR35

Skills & Experience required:

• 3-4 years of relevant experience

Core expected skills (level based on seniority) :

• Python proficiency and familiarity with popular libraries
• Knowledge of frameworks for building complex ML models (e.g. TensorFlow, pytorch or keras)
• Ability to pre-process and feature engineer data (cleaning, transformation, feature extraction)
• Ability to deploy and serve models in prod ready environments (requiring knowledge of containerisation, orchestration, and model serving platforms - docker, Kubernetes, TensorFlow etc).
• Familiar with model interpretability and explainability and techniques to interpret and explain model results (e.g. SHAP, LIME)