Machine Learning Lead (MLOps)

About the role

We are seeking a ML Ops Lead with expertise in Cloud (Preferably Google Cloud Platform - GCP) to join our Machine Learning (ML) team. As one of the senior hires in the team, you will also mentor and guide team members in best practices for MLOps on GCP. ML knowledge is optional.

Our aim is to lead the transformation of global premium brands through the strategic implementation of machine learning. As a ML Ops Lead, you will play a pivotal role in driving this vision by developing and deploying innovative & cutting-edge solutions that seamlessly integrate machine learning algorithms into our business processes to leverage machine learning for maximum operational efficiency and customer satisfaction. 

You will collaborate with cross-functional teams to identify and prioritize ML use cases, architect scalable and reliable ML pipelines, and deploy models in production. With a strong focus on automation, scalability, and reliability, you will ensure that our ML systems are robust, maintainable, and easily deployable. By harnessing the power of ML technologies, our MLOps team aims to drive data-driven decision-making, optimize inventory management, enhance customer personalization, and ultimately, deliver an exceptional shopping experience in the dynamic world of retail and e-commerce.

 

Key Duties and Responsibilities

• Design and implement ML Ops strategies, frameworks, and best practices, with a specific focus on cloud technology.
• Lead the deployment, scaling, and management of machine learning models in a cloud environment, ensuring high availability and reliability.
• Collaborate with data scientists and engineers to develop scalable and efficient ML pipelines and workflows.
• Drive the integration of DevOps principles and practices into the ML Ops workflow, promoting automation, continuous integration, and continuous deployment.
• Develop and maintain infrastructure-as-code (IaC) scripts and templates for provisioning cloud resources.
• Monitor and optimize the performance of machine learning models, addressing scalability, latency, and resource utilization challenges.
• Implement robust data governance and security measures in the cloud environment, ensuring compliance with industry standards and regulations.
• Stay up to date with the latest advancements in cloud technologies and ML Ops methodologies and evaluate their potential impact on our infrastructure and workflows.
• Lead and mentor a team of ML Ops engineers, providing technical guidance and fostering a collaborative environment.

 

Qualifications and Requirements

• Proven experience (5+ years) in ML or software engineering operations, with a strong focus on cloud native (Preferably GCP) development & deployment in python.
• Deep understanding of cloud-based infrastructure and services, including virtual machines, containers, serverless architecture, and distributed computing.
• Proficiency in one or more programming languages commonly used in ML Ops, Python/Java are key but experience with JavaScript/React would be advantageous.
• Hands-on experience with ML Ops tools and frameworks, e.g. as Kubernetes, Docker, Airflow, MLflow, or Kubeflow.
• Strong knowledge of DevOps practices, CI/CD pipelines, and version control systems (Git).
• Familiarity with data storage and processing technologies, including databases (SQL, NoSQL, BigQuery), big data frameworks (Hadoop, Spark) and vector DB’s (Redis, Milvus).
• Excellent problem-solving skills and the ability to troubleshoot complex ML Ops issues in a cloud environment.
• Experience with security and compliance practices in a cloud environment, including encryption, access control, and data privacy.
• Strong leadership and communication skills, with the ability to collaborate effectively with cross-functional teams and stakeholders.
• Ability to mentor and guide junior and mid-level team members

 

Preferred Skills

• Experience with multi-GPU/CPU deployments using Triton Inference Server.
• Experience with infrastructure orchestration tools, such as Terraform.
• Knowledge of machine learning frameworks and libraries, such as TensorFlow, PyTorch, or scikit-learn.
• Understanding of data engineering concepts, including data ingestion, data transformation, and feature engineering.
• Familiarity with monitoring and logging tools, such as Prometheus, Grafana, ELK stack, or CloudWatch.

 

Company Perks and Benefits

• Fully remote work environment with option to work on-site if desired
• Provisional bonuses based on performance
• Staff discounts on THG brands and Hale Country Club
• On-site doctor, physio, and barber

 

Examples of the work team has undertaken

• Machine Translation model that outperforms Google Translate in the 11 languages that we operate
• Bayesian product recommendation operating globally across hundreds of brands.
• Fraud detection model running on millions of transactions across different brands.
• Anomaly detection in transaction data streams.
• Product review sentiment analysis over time per brand.
• Real-time Semantic (Vector) search for products across our brands.

 

Opening Date: 29/06/2023

Closing Date: 13/07/2023

Salary: Competitive