Senior ML Engineer
4 weeks ago
Location: We operate a flexible, hybrid working environment with the candidate required to travel to either our Winchester or London office once or twice a week. We offer
- Up to £80K base salary
- 10% Bonus
- 6% pension contribution
- Private Medical
- 25 days annual leave
- Access to our comprehensive flexible benefits including discounts on big brands, wellness and employee assistance programmes, gymflex, buy and sell annual leave, travel and dental insurance
- Work. Life. Smarter. Our commitment to a flexible and hybrid working culture
- Developing, deploying, and maintaining machine learning models and pipelines using Python, AWS, Databricks and MLOps tools.
- Collaborating with data analysts, data engineers, product owners and business stakeholders to understand the requirements and objectives of various machine learning projects.
- Applying best practices and standards for machine learning development, testing, monitoring and documentation.
- Performing exploratory data analysis, feature engineering, model selection, hyperparameter tuning and evaluation.
- Troubleshooting and resolving issues related to data quality, model performance and scalability.
- Researching and staying updated on the latest trends and technologies in machine learning and data engineering.
- A bachelor’s degree or higher in computer science, engineering, mathematics, statistics or a related field.
- At least 5 years of experience in machine learning and data engineering, preferably in a cloud environment.
- Proficiency in Python and its popular libraries such as pandas, NumPy, scikit-learn, TensorFlow, pytorch, etc.
- Experience in using AWS services such as S3, EC2, Lambda, Sagemaker, Glue, etc.
- Experience in using Databricks and MLOps tools such as MLflow, Airflow, Kubeflow, etc.
- Experience in building end-to-end machine learning pipelines, from data ingestion and processing to model deployment and monitoring.
- Experience in working with structured and unstructured data, such as text, images, audio, video, etc.
- Experience in applying machine learning techniques such as supervised, unsupervised and reinforcement learning, as well as deep learning, natural language processing, computer vision, etc.
- Experience in using SQL and NoSQL databases, such as PostgreSQL, DynamoDB, etc.
- Experience in using version control and collaboration tools, such as GitHub, Jira, etc.
- Excellent communication and presentation skills, both verbal and written.
- A strong analytical and problem-solving mindset, with a passion for learning and innovation.
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