Assistant Principal AI Engineer - MLOps, AI.DA STC

Assistant Principal AI Engineer - MLOps, AI.DA STC

The AI & Data Analytics Strategic Technology Centre (AI.DA STC) is a corporate applied research lab that aims to develop key technologies to support ST Engineering’s global growth plans across all our business sectors.

We seek a driven and passionate individual who can support the team in tackling complex challenges, including designing, implementing, and ensuring the delivery of end-to-end AI/ML systems for clients and relevant stakeholders.

Responsibilities:

• Develop, maintain, and monitor scalable pipelines and machine learning workflows across various platforms
• Design and develop backend and frontend components to enable seamless integration and interaction between differing components of an AI/ML product or application.
• Create and manage environments for AI development and production, ensuring optimal resource allocation and compliance with security standards.
• Implement continuous monitoring mechanisms for AI solutions to ensure performance efficiency, accuracy, and reliability.
• Collaborate with cross-functional teams to implement best practices in code development, data governance, and automated pipelines.
• Contribute to the architecture and advancement of the data and analytics platform, exploring new tools and techniques within distributed environments.
• Integrate and transform data from diverse sources, such as databases, APIs, log files, and streaming platforms to support analytics and machine learning operations.
• Partner with stakeholders to develop solutions using AI/ML, tailored to business needs, ensuring the seamless integration of differing capabilities.

Requirements:

• Experienced in software engineering, with 3+ years of experience in roles that involve the intersection of AI/ML, data engineering, and/or system administration.
• Proven expertise in building scalable solutions.
• Experience with and knowledge of the following:
  • Linux and Unix-based operating systems
  • Version control systems (Git)
  • Containerisation tools (Docker, podman, buildah)
  • virtual environments/machines and dependency management
  • DevOps-related skills (CI/CD, testing, automated pipelines, packaging, etc.)
  • MLOps concepts and tooling (experiment tracking, lineage tracking, data versioning, model deployment, etc.).
  • Observability (Logs, Metrics, Traces, etc.)
  • Networking concepts
  • Infrastructure as Code frameworks/workflows
• Proficiency and hands-on experience in Python and SQL. Familiarity with Typescript or Go would be advantageous.
• Experience and familiarity with distributed tooling.
• Ability to develop and maintain deployments/services within a Kubernetes environment. Familiarity with tools relevant to the Kubernetes ecosystem is expected.
• Experience with batch data processing and data modeling. Familiarity with real-time implementations would be advantageous.
• Understanding and awareness of software and AI engineering best practices.
• Exposure to the Generative AI ecosystem.
• Analytical, problem-solving, and communication skills.

Nice to Have:

• Familiarity with Scrum methodology and agile practices.
• Experience with streaming data technologies such as Kafka.
• Exposure to the Generative AI-centric ecosystem.