Top Machine Learning Jobs in Singapore
Seeking a Principal Machine Learning Engineer to lead the ML/AI team, overseeing all aspects of the ML lifecycle and guiding the development of core ML capabilities. Responsible for architecting and designing ML systems, staying updated with AI advancements, managing POC AI solutions, and providing technical leadership and oversight to AI engineers.
The Software Developer I will design, develop, and implement software products for business projects, collaborating with teams and stakeholders. Responsibilities include coding, testing, quality assurance, and maintaining documentation while staying updated on new technologies and best practices.
As the GenAI Sales Lead, you will engage with executives and partners in the GenAI sector, driving sales cycles for Databricks' AI products. Your role includes developing business plans, mentoring teams, ensuring customer satisfaction, and aligning sales strategies with client needs.
As a Principal Engineer in MLOps, you will lead the design and implementation of machine learning operations, collaborate with cross-functional teams, and mentor engineers. You'll focus on integrating ML workloads into production environments while ensuring reliability and scalability.
The AI/ML and MLOps Field Engineer at Canonical will assist global companies in adopting AI using open source technologies on cloud infrastructures, primarily focusing on Linux and Kubernetes. Responsibilities include architecting cloud solutions, engaging in customer interactions, and solving complex data architecture problems using various data technologies.
The Solutions Architect - AI/ML at Snowflake will design and deploy AI/ML solutions for clients, ensuring successful implementation of data science pipelines using Snowflake's features. This role is technical and collaborative, focusing on customer needs, knowledge transfer, and continuous improvement of Snowflake products.
As a Rust Engineer at Hiveon, you will build infrastructure for AI data centers and develop agents to interface with hardware. Your role will involve extensive programming in Rust and other low-level languages, contributing to a new AI/ML product aimed at democratizing GPU computing.
As a Software (ML Product) Engineer at iterative.ai, you will enhance user workflows for DVC, collaborate with technical product managers, and improve MLOps practices within ML teams. Responsibilities include optimizing the tool’s usability and driving projects like dvclive, emphasizing software engineering skills and communication.
The VP Network DevOps Engineer leads engineering activities, ensuring quality standards are met in technology infrastructure. Responsibilities include serving as a subject matter expert, defining system enhancements, guiding project initiatives, mentoring junior members, and driving compliance with regulations.
The Transaction Monitoring Engine Specialist is responsible for designing and developing AI/ML models for transaction monitoring, analyzing datasets for trends, optimizing monitoring systems, ensuring regulatory compliance, and collaborating with cross-functional teams. They also prepare documentation and provide training on monitoring tools.
The Software Engineer (AI/ML) is responsible for both front and back end programming, designing application architecture, and ensuring application performance and integration with various frameworks. They will collaborate with teams to deliver projects from conception to completion and explore new technologies within Industry 4.0 digital solutions.
The Solutions Architect will design and implement cloud architecture solutions focusing on Ubitus, Tencent Cloud, and AWS. Responsibilities include cloud services integration, providing technical leadership, collaborating with customers on AI requirements, and maintaining technical documentation. The role also involves customer engagement through workshops and training, alongside travel for support and industry events.
The Golang Engineer will contribute to the development of AI and ML products, focusing on decentralized GPU computing. Responsibilities include back-end development, designing complex systems, and ensuring high-load system performance, while collaborating in a fast-paced, innovative environment.
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