Senior AI Engineer - AI Prototyping Lab

📍 Job Overview

Job Title: Senior AI Engineer - AI Prototyping Lab

Company: SAP

Location: Singapore, SG, 117440

Job Type: Regular Full Time

Category: AI Engineering / Software Development

Date Posted: 2026-05-28

Experience Level: 8+ Years (Senior Professional)

Remote Status: Hybrid

🚀 Role Summary

• Drive the innovation and development of an AI Prototyping Lab focused on emerging AI technologies and Agentic AI solutions.

• Lead the creation of prototypes, proof-of-concepts, and pilot solutions to validate business opportunities and accelerate innovation across the enterprise.

• Develop and implement Agentic AI solutions leveraging Large Language Models (LLMs), enterprise systems, APIs, and knowledge sources.

• Build scalable, cloud-native applications, APIs, microservices, and reusable platform capabilities to enhance developer productivity and AI adoption.

• Collaborate closely with Product Owners and Product Managers to define the strategic roadmap and technical direction for AI platform investments.

📝 Enhancement Note: This role is positioned as a Senior AI Engineer within SAP's Enterprise AI CoE, focusing on a dedicated AI Prototyping Lab. The emphasis on "Agentic AI Engineering" and "Full-Stack Engineering & Platform Development" suggests a blend of cutting-edge AI research and practical application within an enterprise context, requiring candidates with a strong foundation in both AI/ML and robust software engineering principles. The hybrid work arrangement indicates a need for on-site collaboration in Singapore while allowing for some remote flexibility.

📈 Primary Responsibilities

• Lead the design, development, and ongoing evolution of the AI Prototyping Lab, ensuring a secure, scalable, and enterprise-ready environment for AI experimentation.

• Drive the creation of prototypes, proof-of-concepts (POCs), and pilot solutions to validate business opportunities and accelerate the adoption of AI technologies.

• Evaluate and integrate emerging AI technologies, frameworks, and development patterns to improve organizational productivity and business outcomes.

• Transform innovative AI concepts into reusable platform capabilities, accelerators, and scalable enterprise solutions.

• Design and build Agentic AI solutions utilizing LLMs, enterprise systems, APIs, and knowledge sources to address complex business challenges.

• Develop AI Agents, multi-agent workflows, MCP-enabled integrations, orchestration patterns, reasoning frameworks, memory strategies, and AI-assisted automation capabilities.

• Establish reusable agent engineering patterns, frameworks, and platform services to expedite the adoption of Agentic AI across SAP.

• Champion best practices for AI governance, observability, security, scalability, and responsible AI implementation within the lab and broader organization.

• Design and develop modern cloud-native applications across frontend, backend, integration, and platform layers, focusing on scalability and resilience.

• Build and deploy scalable APIs, microservices, reusable services, developer tooling, SDKs, and self-service capabilities to enhance developer productivity and platform adoption.

• Deliver intuitive user experiences that simplify the development and consumption of AI-powered capabilities.

• Implement secure, resilient, and maintainable architectures aligned with enterprise standards and engineering best practices.

• Partner closely with Product Owners and Product Managers to shape the vision, roadmap, and strategic direction of the AI Prototyping Lab.

• Provide technical expertise during product discovery, assisting in opportunity evaluation, assumption validation, and identification of innovative AI-driven solutions.

• Support backlog refinement, feature prioritization, roadmap planning, and release discussions, balancing business value, technical feasibility, user experience, and operational considerations.

• Act as a trusted technical advisor in product decision-making and help define scalable implementation approaches for new platform capabilities.

• Drive platform adoption through improved onboarding, developer enablement, reusable templates, and self-service capabilities.

• Establish continuous feedback loops with users and stakeholders to refine platform capabilities, priorities, and adoption strategies.

• Champion engineering excellence, automation, knowledge sharing, and continuous learning within the AI Prototyping Lab team.

• Foster a culture of experimentation, innovation, collaboration, and rapid learning.

📝 Enhancement Note: The responsibilities clearly delineate a dual focus: innovation/prototyping and practical engineering of Agentic AI solutions. The emphasis on "reusable platform capabilities," "developer enablement," and "self-service capabilities" highlights a strategic objective of democratizing AI within SAP, making this role pivotal for broader organizational AI adoption.

🎓 Skills & Qualifications

Education:

• Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, Information Technology, or a related discipline. Experience:

• 8+ years of experience in software engineering, platform engineering, enterprise application development, or AI engineering.

• Demonstrated experience delivering complex technical solutions within cloud-native and enterprise technology environments.

• Strong passion for innovation, emerging technologies, and the practical application of AI to solve business challenges. Required Skills:

• Full-Stack Development: Proven ability to deliver scalable enterprise applications and platform capabilities across frontend, backend, APIs, integrations, and cloud-native architectures.

• Agentic AI Engineering: Deep expertise in AI Agents, multi-agent systems, MCP-based integrations, Retrieval-Augmented Generation (RAG), AI orchestration frameworks, and intelligent automation solutions.

• Generative AI Platforms: Proven experience building Generative AI platforms, reusable services, SDKs, developer tooling, and self-service capabilities that accelerate AI adoption and developer productivity.

• Enterprise AI Architecture & Governance: Strong understanding of enterprise AI architecture, governance, security, observability, and responsible AI principles, with the ability to transform prototypes into production-ready solutions.

• Modern Software Engineering Practices: Experience with DevOps, CI/CD, automation, microservices, containerization (e.g., Docker), Kubernetes, and cloud-native development.

• Product Ownership Support: Proven ability to collaborate effectively with Product Owners, Product Managers, Architects, Engineers, and Business Stakeholders.

• Communication & Stakeholder Engagement: Strong communication skills with the ability to influence both technical and non-technical audiences.

Preferred Skills:

• Familiarity with enterprise AI platforms such as SAP AI Core, SAP AI Launchpad, SAP BTP, Azure AI Services, AWS AI Services, or similar technology ecosystems.

• Demonstrated ability to evaluate emerging technologies, balance technical feasibility with business value, and drive innovation through rapid experimentation and delivery.

• Technical leadership experience, including mentoring engineers, conducting code reviews, and promoting engineering best practices.

• Experience in designing and developing intuitive user experiences for AI-powered capabilities.

• Understanding of enterprise AI adoption patterns and leveraging AI to improve business processes.

• Ability to translate business opportunities into scalable technical solutions with measurable outcomes.

📝 Enhancement Note: The "Technical Expertise" section is exceptionally detailed, indicating a high bar for candidates. The explicit mention of specific SAP platforms (AI Core, AI Launchpad, BTP) and cloud providers (Azure, AWS) suggests these are preferred or essential technologies. The requirement for "8+ years" coupled with "Senior Professional" employment type confirms this is a lead-level role.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Showcase of end-to-end full-stack development projects, demonstrating proficiency across the entire software development lifecycle.

• Examples of AI/ML prototypes or implementations, highlighting innovative applications of AI to solve business problems.

• Case studies demonstrating experience with Agentic AI concepts, such as AI Agents, multi-agent workflows, or intelligent automation.

• Projects that illustrate the development of reusable platform capabilities, SDKs, or developer tooling designed to enhance productivity.

• Demonstrations of cloud-native application development, leveraging microservices, APIs, and containerization technologies. Process Documentation:

• Evidence of designing and documenting scalable architectures, including API specifications, service interactions, and deployment strategies.

• Examples of workflow automation or orchestration, particularly those involving AI agents or complex system integrations.

• Documentation of AI governance, security, or responsible AI implementation strategies within a project context.

• Portfolios that include contributions to CI/CD pipelines, demonstrating an understanding of automated testing and deployment.

📝 Enhancement Note: Given the "Senior" title and focus on platform development and AI innovation, a strong portfolio is crucial. Candidates should be prepared to present detailed case studies of complex projects, emphasizing their role in design, implementation, and the impact of their contributions. The focus on "reusable platform capabilities" and "developer enablement" suggests a need to showcase projects that empowered others.

💵 Compensation & Benefits

Salary Range:

• Based on industry benchmarks for Senior AI Engineers in Singapore with 8+ years of experience, the estimated salary range is SGD 120,000 - SGD 180,000 per annum. This range accounts for the specialized skills in AI, full-stack development, and leadership required for this role at a major technology company like SAP. Benefits:

• Constant Learning: Opportunities for continuous professional development and skill enhancement.

• Skill Growth: Access to training, certifications, and exposure to cutting-edge AI technologies.

• Health And Well-being Support: Comprehensive health insurance and wellness programs.

• Flexible Working Models: Hybrid work arrangement allowing for a balance between in-office collaboration and remote work.

• Comprehensive benefits package typical of a large enterprise organization, likely including:

  • Medical, dental, and vision insurance.

  • Retirement savings plans (e.g., CPF contributions as per Singapore regulations).

  • Paid time off, including vacation, sick leave, and public holidays.

  • Potential for performance-based bonuses and stock grants.

  • Employee Assistance Programs (EAP) for personal and professional support.

  • Opportunities for international collaboration and travel (up to 10%). Working Hours:

• Standard 40-hour work week, with potential for flexibility given the hybrid arrangement.

• Occasional overtime may be required to meet project deadlines or support critical initiatives.

📝 Enhancement Note: Salary estimation is based on publicly available data for senior engineering roles in Singapore, adjusted for the specialized AI and enterprise platform focus. SAP is known for robust benefits, so the listed items are standard offerings for such roles. The "Additional Locations" note of "#LI-Hybrid" reinforces the hybrid nature.

🎯 Team & Company Context

🏢 Company Culture

Industry: Software and Technology, Enterprise Software, Cloud Computing, Artificial Intelligence. SAP is a global leader in enterprise application software, helping organizations manage business operations and customer relations.

Company Size: SAP is a large enterprise, employing over 100,000 people globally, with a significant presence in Singapore. This size offers opportunities for broad impact and career mobility.

Founded: SAP was founded in 1972, bringing a legacy of innovation and stability to the technology sector.

Team Structure:

• The role is within SAP's Enterprise AI CoE (Center of Excellence).

• The AI Prototyping Lab is a dedicated unit focused on innovation and early-stage development.

• The team likely comprises AI Engineers, ML Engineers, Software Engineers, and potentially Product Managers/Owners.

• Reporting structure is likely to a Lead AI Engineer or a Director overseeing AI innovation initiatives. Methodology:

• Data-Driven Innovation: Emphasis on leveraging data and AI to drive business outcomes and innovation.

• Agile Development: Likely adoption of agile methodologies for rapid iteration and delivery.

• Cloud-Native Architecture: Focus on building scalable, resilient solutions on cloud platforms.

• Experimentation-Driven Engineering: Encouragement of rapid prototyping and testing of new technologies.

• Responsible AI: Strong commitment to ethical AI development and deployment.

Company Website: https://www.sap.com/

📝 Enhancement Note: SAP's reputation as a large, established enterprise software provider means the AI Prototyping Lab likely operates within a framework of enterprise governance and scalability requirements, balancing rapid innovation with robust implementation standards. The mention of "Enterprise AI CoE" suggests a strategic focus on integrating AI across SAP's vast product portfolio and customer base.

📈 Career & Growth Analysis

Operations Career Level: This role is designated as a "Senior Professional" with an "8+ years" experience requirement, placing it at a senior individual contributor level. It involves technical leadership, mentorship, and strategic input, positioning it as a key role within the AI innovation pipeline.

Reporting Structure: The Senior AI Engineer will likely report to a manager or director overseeing the AI Prototyping Lab or the broader Enterprise AI CoE. They will collaborate extensively with Product Owners, Product Managers, Architects, and other engineering teams across SAP.

Operations Impact: The Senior AI Engineer's work will directly influence the development and adoption of cutting-edge AI technologies within SAP. By creating a robust AI Prototyping Lab and developing scalable Agentic AI solutions, this role will accelerate innovation, improve developer productivity, and potentially lead to the integration of new AI-powered features into SAP's core products and services, impacting millions of users globally.

Growth Opportunities:

• Technical Specialization: Deepen expertise in Agentic AI, LLMs, and specific AI platforms, becoming a subject matter expert.

• Leadership Progression: Advance into roles such as Lead AI Engineer, AI Architect, or Manager within the AI CoE or related product development teams.

• Cross-Functional Impact: Gain exposure to various SAP business units and product lines, understanding diverse AI application needs.

• Mentorship & Training: Opportunity to mentor junior engineers and contribute to the development of internal AI talent.

• Industry Influence: Contribute to SAP's AI strategy and potentially represent SAP at industry conferences or forums.

📝 Enhancement Note: The "Senior" title and the nature of the work in an "AI Prototyping Lab" suggest a role that offers significant technical depth and potential for leadership. The integration of AI across a company of SAP's scale presents unique challenges and opportunities for impactful career growth.

🌐 Work Environment

Office Type: SAP operates modern, well-equipped office spaces designed to foster collaboration and innovation. The Singapore office is expected to reflect this, with facilities supporting hybrid work.

Office Location(s): Singapore, SG, 117440. This location is a key hub for SAP in the Asia-Pacific region.

Workspace Context:

• Collaborative Spaces: The office will likely feature open work areas, meeting rooms, and collaboration zones to facilitate team interaction and brainstorming.

• Technology Infrastructure: Access to high-performance computing resources, development tools, and robust network infrastructure necessary for AI development.

• Team Interaction: Regular opportunities for face-to-face interaction with team members, product owners, and stakeholders, balanced with remote work flexibility.

• Focus on Innovation: The environment is expected to be dynamic, encouraging experimentation and the exploration of new ideas.

Work Schedule:

• Standard 40-hour work week.

• Hybrid arrangement allows for flexibility in balancing remote work with in-office days for collaboration and team meetings.

• The nature of innovation work may sometimes require dedicated focus time, and the hybrid model supports this.

📝 Enhancement Note: The hybrid nature of the role in Singapore necessitates a well-structured approach to in-office days, likely focused on collaborative activities, team syncs, and high-impact meetings, while remote days can be used for focused coding, research, and individual development tasks.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or Recruiter screen to assess basic qualifications, experience, and cultural fit.

• Technical Screening: A technical interview, potentially involving a coding challenge or discussion of past projects to evaluate core engineering and AI skills.

• Hiring Manager Interview: Deeper dive into experience, leadership capabilities, problem-solving approach, and alignment with team goals.

• Team/Peer Interviews: Interviews with other engineers or team members to assess collaboration skills and technical depth. This may include a presentation of portfolio work.

• Final Interview: Potentially with a senior leader or architect to discuss strategic alignment and long-term vision.

Portfolio Review Tips:

• Curate Select Projects: Focus on 3-5 of your most relevant and impactful projects that showcase full-stack development, AI innovation, and Agentic AI experience.

• Highlight Your Role: Clearly define your specific contributions, responsibilities, and the technologies you utilized.

• Showcase Impact & Metrics: Quantify the outcomes of your projects whenever possible (e.g., performance improvements, efficiency gains, user adoption rates, ROI).

• Structure for Clarity: For AI projects, detail the problem statement, your approach, the AI models/frameworks used, challenges faced, and the solution's architecture.

• Prepare for Live Demos/Code Walkthroughs: Be ready to walk through code snippets or live demos of your projects, explaining design choices and technical implementation.

• Emphasize Reusability: If applicable, highlight projects where you developed reusable components, libraries, or platform features.

Challenge Preparation:

• Coding Challenges: Practice algorithms, data structures, and problem-solving on platforms like LeetCode, HackerRank, or similar, focusing on Python and JavaScript.

• System Design: Prepare for system design questions related to building scalable cloud-native applications, APIs, and AI platforms.

• AI/ML Concepts: Be ready to discuss core AI/ML concepts, Agentic AI principles, LLM applications, RAG, and model deployment strategies.

• Scenario-Based Questions: Anticipate questions about handling technical challenges, collaborating with stakeholders, and prioritizing tasks in an innovation lab setting.

📝 Enhancement Note: Given the technical depth, expect a rigorous interview process. The portfolio review is likely to be a critical component, so candidates should prepare a concise yet comprehensive presentation that demonstrates their ability to deliver complex AI solutions and platform capabilities.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (primary for AI/ML), JavaScript/TypeScript (for full-stack development).

• Cloud Platforms: SAP BTP, Azure AI Services, AWS AI Services.

• Containerization & Orchestration: Docker, Kubernetes.

• CI/CD Tools: Jenkins, GitLab CI, Azure DevOps, or similar.

• Version Control: Git.

Analytics & Reporting:

• Data Visualization: Tools like Power BI, Tableau, or custom dashboards built with frontend frameworks.

• Monitoring & Observability: Prometheus, Grafana, ELK Stack, or cloud-native monitoring services.

CRM & Automation:

• CRM: SAP CRM or related enterprise CRM systems (familiarity is beneficial).

• Automation Tools: Workflow automation engines, scripting for operational tasks.

• Integration Platforms: SAP Integration Suite, or general API management tools.

AI/ML Specific Tools:

• LLM Frameworks: LangChain, LlamaIndex, Hugging Face Transformers.

• AI/ML Libraries: TensorFlow, PyTorch, scikit-learn.

• Vector Databases: Pinecone, Weaviate, Milvus (for RAG implementations).

• MLOps Tools: MLflow, Kubeflow, or similar for model lifecycle management.

📝 Enhancement Note: The "Technical Expertise" section of the job description explicitly mentions many of these tools and technologies. Candidates should be prepared to discuss their proficiency and experience with these specific stacks, especially Python, JavaScript, cloud platforms, containerization, and AI/ML frameworks.

👥 Team Culture & Values

Operations Values:

• Innovation & Experimentation: A culture that encourages exploring new technologies and rapid prototyping to drive future solutions.

• Collaboration & Teamwork: Strong emphasis on working together across disciplines to achieve common goals.

• Customer Focus: Driving business value and delivering solutions that meet customer needs and improve business outcomes.

• Excellence & Quality: Commitment to high standards in software engineering, AI implementation, and platform development.

• Continuous Learning: Encouraging ongoing development of skills and knowledge in the rapidly evolving AI landscape.

• Responsible AI: Commitment to ethical and secure deployment of AI technologies.

Collaboration Style:

• Cross-Functional Integration: Close collaboration with Product Owners, Product Managers, Architects, Security teams, and other engineering departments.

• Open Communication: Encouraging sharing of ideas, feedback, and technical challenges.

• Mentorship: A culture where senior engineers guide and support junior team members.

• Agile & Iterative: Working in sprints or iterations, with regular feedback loops and adjustments.

📝 Enhancement Note: SAP's stated values ("We help the world run better," "We win with inclusion") and the description of the AI CoE ("where innovators shape, govern, and scale AI") suggest a culture that balances large-scale enterprise operations with a forward-thinking approach to innovation. The emphasis on "inclusion" is also a key cultural pillar.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving AI Landscape: Keeping pace with the constant advancements in AI technologies and identifying relevant, impactful applications.

• Enterprise Integration: Navigating the complexities of integrating new AI capabilities into a large, established enterprise software ecosystem.

• Balancing Innovation and Governance: Ensuring that rapid prototyping and experimentation align with enterprise security, compliance, and responsible AI standards.

• Scalability of Prototypes: Transforming experimental prototypes into robust, scalable, and maintainable enterprise-grade solutions.

• Driving Adoption: Effectively promoting and enabling the adoption of new AI tools and capabilities across diverse engineering teams and business units.

Learning & Development Opportunities:

• Cutting-Edge AI Technologies: Direct exposure to and hands-on experience with the latest in Agentic AI, LLMs, and AI platform development.

• Enterprise AI Strategy: Opportunity to influence and contribute to SAP's overarching AI strategy.

• Cross-Disciplinary Learning: Gaining insights into various business domains and how AI can solve their specific challenges.

• Professional Certifications: Potential to pursue certifications in cloud platforms, AI/ML, or specific SAP technologies.

• Internal Knowledge Sharing: Participating in and leading internal workshops, tech talks, and knowledge-sharing sessions.

📝 Enhancement Note: The challenges are inherent to working at the forefront of AI within a large enterprise. The growth opportunities are significant, offering a chance to shape the future of AI at SAP.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex AI prototyping project you led. What was the business problem, your approach, the technologies used, and the outcome?"

• "How would you design an Agentic AI system to automate a specific business process within an enterprise context? Discuss the components, data flow, and potential challenges."

• "Explain your approach to evaluating emerging AI technologies for enterprise adoption. What criteria would you use?"

• "How do you balance rapid experimentation with the need for enterprise-grade security, scalability, and governance in AI development?"

• "Walk me through a situation where you had to influence stakeholders (technical or non-technical) to adopt a new technology or approach. What was your strategy?" Company & Culture Questions:

• "What interests you about SAP and this specific role within the Enterprise AI CoE?"

• "How do you stay updated with the latest advancements in AI and machine learning?"

• "Describe your experience working in a hybrid environment. How do you ensure effective collaboration?"

• "How do you approach mentoring junior engineers and fostering a culture of continuous learning?"

• "What does 'Responsible AI' mean to you, and how would you ensure it in your projects?" Portfolio Presentation Strategy:

• Focus on Impact: Begin each project presentation with the business problem and the measurable impact achieved.

• Technical Depth: Be prepared to dive deep into the architecture, algorithms, and key technical decisions made.

• Role Clarity: Clearly articulate your specific contributions and leadership where applicable.

• Future State: Discuss any lessons learned and potential future enhancements or next steps for the project.

• Conciseness: Aim for 5-7 minutes per project to allow for discussion.

📝 Enhancement Note: Expect questions that probe both technical depth and strategic thinking. Demonstrating an understanding of enterprise AI challenges and SAP's business context will be crucial.

📌 Application Steps

To apply for this Senior AI Engineer position:

• Visit the SAP Careers website and locate the job posting for "Senior AI Engineer - AI Prototyping Lab."

• Submit your application through the provided online portal.

• Tailor Your Resume: Highlight experience in full-stack development, Agentic AI, LLMs, cloud-native architectures, and any relevant SAP technologies. Quantify achievements where possible.

• Prepare Your Portfolio: Select 2-3 key projects that best showcase your expertise in AI prototyping, Agentic AI engineering, and platform development. Be ready to present them clearly and concisely.

• Research SAP's AI Strategy: Familiarize yourself with SAP's approach to AI, their AI products (e.g., SAP AI Core, SAP AI Launchpad), and their commitment to responsible AI.

• Practice Interview Questions: Prepare for technical, behavioral, and system design questions, focusing on your experience in similar roles and environments.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.