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Machine Learning Engineer

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Hire Machine Learning Engineer
for up to 60% less

Average Salary in the UK £7500.00/month
Omni Talent

£3187.50/month

Average omni talent Salary
57.50% Potential Savings
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Machine Learning Engineer Job Description

Tasks:

  • Model Development & Implementation:
    • Design, build, and optimise machine learning models for classification, regression, clustering, and deep learning tasks.
    • Develop and fine-tune algorithms for predictive analytics, recommendation systems, NLP, and computer vision applications.
    • Utilise frameworks such as TensorFlow, PyTorch, and Scikit-learn to train and evaluate models.
    • Implement feature engineering, selection, and model validation techniques to improve model accuracy and performance.
  • Data Processing & Engineering:
    • Collect, clean, and preprocess structured and unstructured data from various sources.
    • Implement scalable data pipelines using tools such as Apache Spark, Airflow, and Pandas.
    • Perform exploratory data analysis (EDA) to identify trends and patterns that enhance model development.
  • Model Deployment & Monitoring:
    • Deploy machine learning models to production environments using cloud platforms (AWS, Azure, GCP).
    • Build and integrate machine learning solutions into web services and applications via RESTful APIs.
    • Implement MLOps best practices to automate deployment, monitoring, and model retraining workflows.
    • Use tools such as Docker, Kubernetes, and TensorFlow Serving for model packaging and deployment.
  • Performance Optimisation:
    • Optimise model inference time, memory usage, and scalability to support real-time applications.
    • Apply techniques such as model quantisation, pruning, and knowledge distillation to improve performance.
    • Conduct A/B testing and evaluation using metrics such as accuracy, F1-score, and ROC-AUC.
  • Collaboration & Documentation:
    • Work closely with data scientists, software engineers, and business analysts to integrate ML models with business processes.
    • Document model architectures, performance metrics, and deployment processes.
    • Communicate findings and recommendations effectively to both technical and non-technical stakeholders.
  • Research & Innovation:
    • Stay up-to-date with the latest developments in machine learning and AI technologies.
    • Experiment with cutting-edge techniques such as transfer learning, reinforcement learning, and generative AI.
    • Contribute to open-source projects and participate in AI conferences and communities.

Qualifications:

  • Proven experience as a Machine Learning Engineer or related role.
  • Proficiency in programming languages such as Python, R, or Scala.
  • Strong understanding of machine learning algorithms, statistical methods, and deep learning architectures.
  • Experience with cloud computing platforms (AWS, Azure, GCP) and their AI/ML services.
  • Familiarity with version control systems (Git), CI/CD pipelines, and containerisation technologies (Docker, Kubernetes).
  • Knowledge of database systems (SQL, NoSQL) and data pipeline tools (Kafka, Spark, ETL frameworks).
  • Strong problem-solving skills and the ability to work independently and collaboratively in a fast-paced environment.
  • Excellent communication skills, with the ability to present complex technical concepts clearly.

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