Job Title:
Machine Learning Engineer, Machine Learning Frameworks & Operations
Job Summary
We are seeking a skilled and motivated Machine Learning Engineer to join our growing team that delivers enterprise-grade MLOps capabilities to clients. The Machine Learning Engineer will play a key role in designing, building, and deploying enterprise-grade machine learning components and deployment pipelines. This role is critical to scaling AI capabilities across large organizations delivering secure, reliable, and repeatable ML workflows from experimentation through to production.
The ideal candidate will bring strong technical expertise in machine learning engineering and MLOps practices, with hands-on experience building and maintaining pipelines, automating deployments, and ensuring compliance with governance and monitoring standards. This is a hands-on technical role that will directly contribute to building MLOps capabilities alongside data scientists, platform engineers, data engineers and risk teams.
Key Responsibilities
MLOps & Pipeline Automation – Technical Contribution
· Design, build, test, enhance and maintain machine learning components, CI/CD pipelines, E2E MLOps workflows, frameworks, automated ML pipelines for data ingestion, training, validation, and deployment. and other repeatable standardized capabilities
· Design compliant workflows with approval gates, automated monitoring and alerts and audit ready processes
· Develop retraining workflows and monitor model performance post-deployment, fit observability stacks to requirements to ensure reliability and compliance.
· Build and scale compliant workflows using Databricks (or similar), machine learning frameworks, Azure, Git and other ML tooling.
Capability Building – Partnership
· Collaborate with data teams to build and leverage quality data pipelines for use in AI solutions
· Work closely with developers and technology teams to build repeatable processes to package models and manage deployment environments.
· Design & Implement processes, components and workflows to ensure reproducibility and traceability across the ML lifecycle.
· Ensure compliance of solutions with governance policies such as Model Risk Management (MRM), Compliance, and audit to align pipelines with regulatory expectations relevant to our clients.
· Work closely with data engineers, DevOps, and product owners to integrate ML pipelines into production systems and align engineering deliverables with developer requirements.
· Submit to and participate in code reviews, contribute to team documentation, and share best practices.
· Stay current with emerging ML tools and techniques, actively contribute to a culture of experimentation and continuous improvement.
Qualifications
Required
· 3 years of experience as a Machine Learning Engineer or in a related role such as Data Engineer or AI developer with hands-on experience operationalizing models in production ML systems
· Hands-on experience building and deploying ML pipelines in a cloud environment (e.g., Azure, AWS).
· Proficiency in Python and ML frameworks such as scikit-learn, TensorFlow, and PyTorch.
· Solid understanding of CI/CD, model monitoring, and model governance best practices.
· Degree in Computer Science, Statistics, Engineering, or a related field.
Preferred
· Experience working in financial services or other regulated industries.
· Familiarity with responsible AI practices and model interpretability techniques.