Principal AI Engineer - MLOps

ST ENGINEERING IHQ PTE. LTD.; Singapore; On-site; SGD 90,000 - 120,000; Full time

Job summary

A leading engineering solutions provider in Singapore seeks an experienced software engineer to develop scalable AI/ML systems. The ideal candidate has over 6 years of experience, proficiency in Python and SQL, as well as familiarity with containerisation tools. This role involves collaborating with cross-functional teams and integrating data from diverse sources to support business needs.

Qualifications

• 6+ years of experience in AI/ML and data engineering.
• Proficiency in Python and SQL, familiarity with Typescript or Go is a plus.
• Experience with DevOps-related tasks and CI/CD.

Responsibilities

• Develop and maintain scalable AI/ML pipelines.
• Collaborate with cross-functional teams for best practices.
• Integrate data from various sources for analytics.

Skills

Software engineering experience

Building scalable solutions

Python

SQL

Containerisation tools

DevOps skills

MLOps concepts and tooling

Kubernetes

Analytical skills

Problem-solving

Tools

Docker

Git

Kafka

Job description

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 6+ 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.