As a Responsible AI Engineer, you’ll be experienced in building state of the art AI systems to solve difficult business challenges. But you’ll also have at least a year’s experience looking at responsible aspects such as AI model Fairness, Transparency, Explainability, Robustness, Soundness and Privacy – preferably gained in a client-facing environment.
You’ll be a problem solver who is passionate about all things data. You’ll be excited by difficult modelling challenges, and you’ll enjoy working with like-minded people who thrive on pushing the boundaries of the possible. You’ll be an outstanding technical specialist with the strong communication skills needed to liaise with your clients and your colleagues.
At the forefront of the industry, you’ll help make our Responsible AI vision a reality for clients looking to better serve their customers and operate always-on enterprises. We’re not just focused on increasing revenues – our technologies and innovations are making millions of lives easier and more comfortable. But above all, we’re doing this responsibly and inclusively – to make sure AI technology is used equitably and in a way that is both ethically and technically sound.
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Cross-functional project and stakeholder management. Lead and coordinate academic, operational, outreach, compliance, grantor, and partner-facing workstreams to align teams around shared milestones, manage dependencies and risks, resolve issues, and deliver the agreed outcomes.
Learner journey and programme operations. Oversee key learner-facing processes, including programme communications, onboarding, schedules, platforms, systems, facilities, attendance, learner progress tracking, and administrative support.
Outreach and learner recruitment. Coordinate outreach and learner recruitment efforts with marketing, admissions, academic leads, industry partners, and other stakeholders, including information sessions, events, lead follow-up, applicant communications, and conversion tracking, to support achievement of programme enrolment targets.
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Training and Development: Develop and deliver training programs to educate MFG teams on AI concepts, tools, and best practices, fostering a culture of AI experimentation and learning within MFG APAC
7 to 10 years of robust experience in enterprise technical architecture, cloud infrastructure provisioning, or highly technical product management.
Deep understanding and hands-on experience with generative AI models, particularly Google Gemini, and a proven ability to leverage these capabilities for advertising-specific applications (e.g., content creation, campaign optimization, audience insights).
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Train, tune, and deploy deep learning models for object detection and semantic segmentation
Calibrate and tune perception sensors (LiDAR, RGBD, stereo cameras) for robust performance in real-world conditions
Collaborate closely with teammates from navigation and SLAM teams to deliver high-quality autonomous software stacks that powers next generation cleaning robots.
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The position supports the development and deployment of AI-enabled applications by assisting with data preparation, AI model integration, prompt optimization, workflow automation, and API implementation. It evaluates AI model performance, validates outputs, and recommends improvements to enhance accuracy, scalability, and operational efficiency.
In addition, the role develops and maintains technical documentation, user guides, troubleshooting procedures, and knowledge base articles to improve support quality and operational consistency. It collaborates with software developers, data engineers, product managers, and infrastructure teams to resolve technical challenges and deliver continuous product enhancements.
The role monitors emerging technologies in artificial intelligence, cloud computing, automation, and machine learning operations (MLOps) to recommend innovative solutions that improve system capabilities, deployment efficiency, and customer satisfaction.
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Review frontier AI and LLM research papers, reproduce relevant methods, and translate research findings into practical improvements in model training, evaluation and deployment workflows.
Contribute to reusable engineering assets such as training scripts, evaluation tools, model artefacts, APIs, documentation and deployment-ready components.
Collaborate with internal teams, external research labs, academic institutions, industry partners and ecosystem stakeholders on LLM research, development and knowledge-sharing activities.
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Work closely with marketing team for new launch product forecasting to ensure sufficient stocks to achieve sell-in and sell-out targets
Provide weekly and monthly sales results analysis for both sell-in and sell-out and develop strategies and action plans to improve performance and close the sales gap between actual vs budget.
Discuss with Assistance Retail & Education Manager to identify, negotiate and secure external promo sites in Sephora to achieve brand/launch objectives
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Partner closely with IC design teams to ensure to deliver comprehensive technical specifications, rigorous benchmark reports, and high-value patent proposals.
Education: Ph.D. in Computer Science, Electronic Engineering, Automation, or a highly related technical field.
Technical Expertise: Deep foundational knowledge in computer architecture, specifically regarding GPU/NPU microarchitecture implementation.
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Possess a Master's degree or PhD in Computer Science, Statistics, Applied Mathematics, or equivalent research experiences.
Demonstrate the ability to generate new ideas and innovate.
Academic or industrial working experiences in recommendation & search, natural language processing, deep learning, machine learning, or related fields.
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Manage day-to-day activities associated with various initiatives, including engagement and relationship building, programmes management, budgeting and outcomes tracking and measurement.
Manage any other duties as assigned in relation to programmes and partnerships, including internal partnerships within the NTUC ecosystem and external partnerships with Government-related organizations.
Apply Practical ML : Implement data analytics, forecasting models, and light machine learning where they materially elevate enterprise decision quality.
Enforce Best Practices : Drive rigorous software engineering standards, including CI/CD, automated testing, version control, infrastructure automation, and clear technical documentation.
Ensure Alignment : Guarantee all data and AI solutions fully comply with group technology standards, security frameworks, and architectural guardrails.
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Manage and coordinate the end-to-end recruitment process including job postings, candidate sourcing, resume screening, interview scheduling, and onboarding of new hires.
Develop, review, and maintain accurate job descriptions and HR documentation in alignment with organizational needs.
Administer employee onboarding and offboarding processes to ensure a structured and positive employee experience.
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