Description
As a Data & ML Platform Engineer, you will play a critical role in designing, building, and maintaining the infrastructure that powers our data processing pipelines and machine learning workflows. You will work at the intersection of data engineering, MLOps, and platform development to ensure our data & AI infrastructure is robust, scalable, and efficient.
Responsibilities:
- Design and implement scalable data and ML infrastructure using modern cloud and on-prem distributed technologies and frameworks.
- Develop scalable and efficient data pipelines to collect, process, and store large volumes of data from various sources.
- Manage and optimize databases and data warehouses to ensure data integrity, performance, and availability.
- Integrate data from multiple sources, including APIs, databases, and external data providers, to create unified datasets for analysis.
- Develop self-service tools that enable stakeholders to extract insights from Data and Machine Learning models.
- Support the expansion of our Data & AI Platform.
- Ensure data security, privacy, and compliance.
- Implement data validation and quality assurance processes to ensure the accuracy and consistency of data.
- Collaborate with data scientists, analysts, and other stakeholders to understand platform requirements and provide necessary data infrastructure and support.
- Implement monitoring, observability, and alerting for data and ML systems and optimize for performance, reliability, and cost-effectiveness.
- Maintain comprehensive documentation of platform components, APIs, and workflows.
Requirements:
- Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related field.
- 3+ years of experience in data & ML platform engineering or a similar role.
- Proficiency in programming languages such as Python, Go, Java, or Rust.
- Strong knowledge of containerization technologies and orchestration systems in production environments.
- Experience with SQL, database management systems, data modeling, and schema design.
- Experience with cloud platforms and their data services, with a focus on Google Cloud.
- Familiarity with big data technologies, data warehousing solutions, data pipeline orchestration, and database systems.
- Design and manage MLOps pipelines to support model training, deployment, and monitoring at scale.
- Experience implementing observability stacks and logging pipelines.
- Experience with IaC frameworks.
- Understanding of DevOps best practices and tools.
- Affinity and experience with AI-assisted development tools.
- Strong analytical and problem-solving skills with a focus on delivering scalable and efficient data solutions.
- Excellent verbal and written communication skills.
- High attention to detail and a commitment to ensuring data quality and accuracy.
- Ability to work in a fast-paced, dynamic environment and manage multiple priorities simultaneously.
What we offer:
- A dynamic and collaborative working environment.
- A competitive salary package.
- Meal allowance and health insurance for employees.
- Bonus in line with company policy.
- Access to the Employee Assistance Programme (EAP).