MLOps Engineer

at  Pandora Jewelry

We have recently embarked on a transformative journey in the realm of advanced analytics, venturing into untapped territories across consumer engagement, supply chain, merchandising, and manufacturing domains. Our focus is on cultivating in-house expertise in areas such as personalization & recommendation, forecasting models, and trend analysis, aiming to elevate our innovation and customer satisfaction to unprecedented heights.
As an MLOps Engineer, you will play a crucial role in designing, developing, and maintaining the infrastructure and platform essential for scaling our machine learning systems efficiently from experimentation to production. Your contribution will be pivotal in enabling and automating data-driven decision-making processes, utilizing cutting-edge techniques and ensuring the seamless integration of machine learning models into our operational workflows.

Responsibilities

• Design, implement and maintain the ML platform and infrastructure required for ML systems to scale
• Facilitate an efficient ML route to live from experiment to production.
• Develop and deploy scalable tools and services to handle machine learning training and inference
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of ML systems
• Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
• Support model development, with an emphasis on auditability, versioning, and data security
• Communicate with product team and data scientists and machine learning engineers to build requirements and track progress

QUALIFICATIONS

• Experience building end-to-end systems as a Platform Engineer, DevOps Engineer
• Strong software engineering skills in complex, multi-language systems
• Fluency in Python
• Experience working with Infrastructure as Code, e.g. Terraform
• Experience working with cloud computing and database systems (SQL, no-SQL, distributed systems, data lakes, etc) such as Databricks
• Experience building custom integrations between cloud-based systems using APIs
• Experience developing with containerisation (e.g Docker) and orchestration Kubernetes in cloud computing environments
• Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)
• Ability to translate business needs to technical requirements
• Strong understanding of software testing, benchmarking, and continuous integration
• Experience in infrastructure, data and model monitoring & alerting (e.g. tools like NewRelic, Prometheus, Grafana, ELK Stack)
• Exposure and understanding of FinOps best practices for the public cloud
• Exposure to machine learning methodology and best practices

Preferred but not must have:

• Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)

EDUCATION & EXPERIENCE

• 2–5 years experience building production-quality software.
• Bachelors or Masters degree and/or equivalent professional experience

• Design, implement and maintain the ML platform and infrastructure required for ML systems to scale
• Facilitate an efficient ML route to live from experiment to production.
• Develop and deploy scalable tools and services to handle machine learning training and inference
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of ML systems
• Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
• Support model development, with an emphasis on auditability, versioning, and data security
• Communicate with product team and data scientists and machine learning engineers to build requirements and track progres