18 days ago
London, United KingdomSenior
Responsibilities
- Define and implement machine learning projects from conception through data preparation, model engineering, evaluation, deployment, monitoring, and maintenance.
- Establish and develop MLOps frameworks and standards within client infrastructure.
- Advise and upskill clients while helping them take ownership of delivered solutions.
- Assess clients’ cloud and AI environments and recommend improvements.
- Build ML strategy blueprints and advise clients on technology options.
- Translate functional and non-functional business requirements into compliant ML solutions and help define policies and standards.
- Identify risks and mitigations for ML and data science programs and support migration from on-premises to cloud infrastructure.
- Advise on ML model governance, including fairness, transparency, interpretability, and accountability.
Requirements
- Advanced degree in computer science, mathematics, physics, engineering, or a related STEM field.
- Strong grounding in applied statistics, classical machine learning, traditional ML algorithms, transformers, and state-of-the-art deep learning.
- Proven ability to build machine learning models and pipelines using Python and common ML and deep learning libraries such as PyTorch and TensorFlow.
- Experience designing, deploying, and maintaining scalable production ML solutions using modern frameworks and software engineering best practices.
- Experience with version control, testing, MLOps, CI/CD, and API design.
- Hands-on production experience with at least one of AWS, Azure, or GCP and experience with ML platforms such as AWS SageMaker or Azure Machine Learning Studio.
- Interest in building forecasting tools, image recognition applications, and LLM-based chatbots and agents.
- Ability to combine technical delivery with consulting, collaboration, communication, and client-facing engagement skills.
- Demonstrated commitment to continuous learning and developing the skills of others.
Tech Stack
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