Senior Machine Learning Engineer

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Requirements:

• Strong experience building and managing MLOps pipelines using frameworks like Kubeflow, MLflow, or similar.
• Proficiency in Python, focusing on writing high-performance, maintainable code.
• Hands-on experience with AWS services (e.g., S3, EC2, SageMaker), and infrastructure-as-code tools like Terraform.
• Deep understanding of Docker and container orchestration tools like Kubernetes.
• Experience designing scalable ETL pipelines and working with SQL and NoSQL databases.
• Familiarity with API integrations, network configurations (e.g., proxies, SSH, NAT, VPN), and security best practices.
• Knowledge of monitoring tools such as Prometheus, Grafana, or CloudWatch.

Responsibilities:

• Design, build, and maintain robust MLOps pipelines to support the end-to-end lifecycle of machine learning models, including data preparation, training, deployment, monitoring, and retraining.
• Develop reusable tools and modules to enable efficient experimentation, model deployment, and versioning.
• Integrate ComfyUI nodes and other workflow tools into the MLOps ecosystem, optimising for performance and scalability.
• Collaborate with DevOps teams to implement and manage cloud infrastructure, focusing on AWS (e.g., S3, EC2, SageMaker) using tools like Terraform and CloudFormation.
• Implement CI/CD pipelines tailored for machine learning workflows, ensuring smooth transitions from research to production.
• Optimise resource allocation and manage costs associated with cloud-based machine learning workloads.
• Design and maintain scalable data pipelines for collecting, processing, and storing large volumes of data.
• Automate data acquisition and preprocessing workflows, optimising I/O bandwidth and implementing efficient storage solutions.
• Manage data integrity and ensure compliance with privacy and security standards.
• Deploy machine learning models to production, ensuring robustness, scalability, and low latency.
• Implement monitoring solutions for deployed models to track performance metrics, detect drift, and trigger retraining pipelines.
• Continuously optimise inference performance using techniques like model quantisation, distillation, or caching strategies.
• Work closely with cross-functional teams, including AI researchers, data engineers, and software developers, to support ongoing projects and align MLOps efforts with organisational goals.
• Proactively identify opportunities to streamline and automate workflows, driving innovation and efficiency.
• Operate independently to manage deadlines, deliverables, and high-quality solutions in a dynamic environment.