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Company Overview
This is a global technology, defense, and engineering group headquartered in Singapore. It’s a major player in various sectors, leveraging technology and innovation to solve real-world problems and contribute to a more secure and sustainable world.
Assistant Principal AI Engineer – MLOps, AI.DA STC
This role seeks an accomplished AI Engineer to contribute to the development and operationalization of machine learning solutions. The successful candidate will play a pivotal role in building and maintaining robust AI/ML infrastructure and workflows.
Responsibilities:
Developing, maintaining, and monitoring scalable pipelines and machine learning workflows across various platforms.
Designing and developing backend and frontend components to enable seamless integration and interaction between differing components of an AI/ML product or application.
Creating and managing environments for AI development and production, ensuring optimal resource allocation and compliance with security standards.
Implementing continuous monitoring mechanisms for AI solutions to ensure performance efficiency, accuracy, and reliability.
Collaborating with cross-functional teams to implement best practices in code development, data governance, and automated pipelines.
Contributing to the architecture and advancement of the data and analytics platform, exploring new tools and techniques within distributed environments.
Integrating and transforming data from diverse sources, such as databases, APIs, log files, and streaming platforms to support analytics and machine learning operations.
Partnering 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
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.
Disclaimer
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