DevOps Engineer

• Design, implement, and optimize CI/CD pipelines for continuous integration and delivery. 

• Ensure efficient build, test, and deployment workflows for multiple development teams. 
   

• Design, deploy, and manage applications using Kubernetes, Docker, Helm, and related technologies. 

• Manage containerized applications in a cloud-native environment (Azure, etc). 

• Implement best practices for ingress, networking, and TLS termination. 

• Act as feature owners, coordinating with both internal and external stakeholders to fulfill the feature lifecycle. 

• Resolve bugs and work on stories within sprints, ensuring delivery of high-quality solutions. 

• Foster innovation, modernization, and the reduction of complexity. 

• Collaborate with JB platform engineering teams (requirements, piloting solutions) and serve dev agile teams 

• Conduct Proofs of Concept (POC) to explore and pioneer new technologies for the bank. 

• Serve as pioneers within the bank for adopting and adapting to new systems and tools. 

• Embrace automation over manual processes to improve team productivity and efficiency. 

• Advocate for an “everything as code” approach 

• Implement observability tools to monitor system performance and ensure uptime (Prometheus, Grafana, Dynatrace, etc.). 

• Set up logging, tracing, and alerting to proactively identify and resolve issues. 

• Use deployment tools such as Octopus Deploy, GitLab, Jenkins, etc to manage releases. 

• You have a can-do attitude, are pragmatic, and open-minded.  

• Strong knowledge of Continuous Integration (CI)/Continuous Delivery (CD) pipelines, cloud environments, containerization (Docker/K8s), and APIs. 

• Proven experience and strong knowledge of DevOps, with a strong understanding of CI/CD, containerization, and cloud platforms. 

• You take pride in your work and strive to lead by example.  

• Comfortable working in agile and cross-functional teams, with excellent communication and collaboration skills. 

• Master or Bachelor in Computer Science or a related area or proven relevant working experience. 

• Strong communications in English

• Programming Languages: Proficiency in .NET (C#) or Java. 

• Scripting: Strong experience with PowerShell, Bash, or Python for automation.  (nice to have) 

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

• Containerization & Orchestration: Kubernetes, Docker, Helm. 

• Databases : Microsoft SQL Server, Oracle, etc 

• Observability & Monitoring: Prometheus, Grafana, Dynatrace, ELK Stack, Splunk, etc. 

• Cloud Platforms: Experience with Azure, AWS, or Google Cloud Platform. 

• Deployment Tools: Octopus Deploy, Terraform, or similar. 

• Version Control: Strong knowledge of Git for version control and collaboration. 

• Networking: Understanding of ingress controllers, load balancers, and basic networking principles. (nice to have) 

• Conceptualise new initiatives to provide assurance on GenAI product adoption, ensuring testing strategies drive measurable commercial impact for leading GenAI companies.
• Lead validation of LLM, agentic, and GenAI products within secure, enterprise-grade sandbox environments. Provide advanced AI testing tools and services to elevate product quality and reduce adoption risk.
• Design and implement structured evaluation frameworks covering agent stacks, model configurations, prompt libraries, tool chains, and integration endpoints. Assess reasoning, planning, tool usage, autonomy, and safety across government and enterprise use cases.
• Analyse GenAI product architecture to identify core tech differentiators. Conduct deep-dive assessments to guide QA strategy.
• Collaborate with company technical leaders to develop comprehensive test plans targeting hallucinations, risk-based scenarios, reliability, and edge-case behaviours of agent workflows.
• Use outsourced testing for functionality, performance, and security. Recommend actionable improvements to fix defects and recurring issues, such as reasoning errors.
• Conduct ongoing market research to track new agentic frameworks, evaluation methods, and GenAI QA tools. Provide technical advisory to accelerate GenAI product adoption.
• Drive Proof-of-Concepts (POC) with potential buyers. Support Tech Acceleration Lab (TAL) by designing practical, scalable solutions for pilots and enterprise deployments.

• Bachelor’s or Master’s degree in Computer Science, IT, or related field.
• Certification in related areas such as:
  • ISTQB Certified Tester AI Testing (CT-AI)
  • ISTQB Certified Tester Testing with Generative AI (CT-GenAI)
  • GSDC Certified AI Testing Professional (CAITP)

• 8 - 12 years of ICT/ICM experience in Product Quality & Solution Architect; and at least 5 - 8 years of experience with AI and GenAI product testing.
• Strong knowledge of LLM, RAG, agentic frameworks, MCP and vector retrieval workflows.
• Experience with testing for hallucination, reasoning accuracy, safety guardrails and tool execution reliability.
• Knowledge of ISO standards e.g. ISO 17025 / 25010 / 27001 and OWASP – ASVS, etc; and IM8 Government Policy related to software quality, security, and data handling.
• Clear written and verbal communication skills to unpack complex technical details to diverse audiences.

• Self-motivated individual with high degree of personal initiative, flexibility and positive attitude to adapt to dynamic scope of activities.
• Excellent interpersonal skills to coordinate with diverse stakeholders (tech and non-tech) to effectively interact with key stakeholders (e.g. CXOs) to be able to engage in strategic discussions.
• Analytical skill to evaluate risk, constrains and propose innovation solution.
• Plugged into the ICT tech, start-up and/or R&D ecosystem in Singapore.
• Commercially-savvy with excellent communication skills.

Key Responsibilities

• Lead the end-to-end prototyping lifecycle for data and AI systems, from concept design to Proof-of-Concept development.

• Develop and refine AI prototypes using novel techniques, with emphasis on LLM development, fine-tuning, optimisation, and real-world integration.

• Produce clear, detailed technical documentation for experiments and prototypes.

• Support and run innovation challenge activities to surface new ideas and prototype approaches.

• Contribute technical thought leadership through papers, blogs, and presentations on AI best practices.

• Participate in demos, technical showcases, and internal knowledge-sharing sessions.

• Engage with global research and industry partners to stay current with emerging trends and represent the team at international forums.

Requirements

You should have at least 5 years of experience in two or more of the following domains:

AI Product Engineering / Prototyping

• Translating real-world problems into AI/ML problem statements.

• Building ML/LLM models using modern AI libraries and techniques.

• Deploying AI workflows to enhance product development or operational processes.

• Assessing the effectiveness, scalability, and sustainability of AI workflows.

Data Engineering

• Hands-on experience with data collection, ingestion, storage, transformation, and pipeline design.

• Ability to map complex data sources, design data flows, and integrate multiple streams into usable datasets.

• Familiarity with data management and optimisation practices.

AI Ethics & Governance

• Applying ethical guidelines and governance frameworks in AI development.

• Ensuring responsible and compliant use of AI in projects.

Additional Attributes

• Experience commercialising or translating emerging technologies into practical applications.

• Strong understanding of the practical limitations and challenges in AI and data systems.

• Passion for hands-on experimentation and rapid prototyping.

• Interest or experience in LLM development, model fine-tuning, agentic workflows, or generative AI applications.

• Strategy & Leadership
• Define and drive the organization’s vision and strategy for generative AI, ensuring alignment with overall business objectives.
• Identify opportunities for innovation and business value creation through AI-driven insights and solutions.
• Develop and maintain a roadmap for AI adoption, piloting initiatives, and driving adoption across various business units.
• Solution Development & Implementation
• Lead cross-functional teams of data scientists, engineers, product managers, and domain experts to design, build, and deploy generative AI-driven solutions that address key business challenges.
• Develop and maintain an enterprise-wide Generative AI platform, ensuring it is scalable, secure, compliant, and easily accessible to different business units.
• Oversee large-scale AI projects, ensuring technical feasibility, budget management, and timely delivery.
• Guide the development of proof-of-concepts (PoCs) and MVPs, creating scalable AI solutions that can be integrated into the organization’s technology stack.
• Team Building & Management
• Attract, hire, and retain top AI talent, developing an environment that fosters creativity, collaboration, and continuous learning.
• Coach and mentor team members, providing professional development opportunities and skill-building initiatives.
• Establish agile project management practices, promoting high efficiency, accountability, and transparency.
• Stakeholder Engagement
• Collaborate with senior leadership, department heads, and external partners to identify business needs and evaluate AI solution viability.
• Communicate complex AI concepts in clear, business-oriented language to stakeholders at all levels.
• Build and maintain relationships with technology vendors, business lines and other external parties for potential partnerships and co-innovation projects.
• Governance & Ethics
• Define and implement best practices for AI ethics, data privacy, and responsible AI governance.
• Ensure compliance with relevant regulations and internal policies.
• Establish standards and guidelines for model deployment, monitoring, and risk management.
• Performance Measurement & Continuous Improvement
• Develop KPIs and metrics to measure the success, impact, and ROI of AI initiatives.
• Implement processes for ongoing evaluation and optimization of AI models and processes.
• Cultivate a data-driven culture and champion continuous improvement across all AI-related activities.
  
   

• Education
  • Background in computer science, Machine Learning, Data Science, or a related field preferred. Equivalent experience in AI leadership and innovation may be considered.
• Experience
  • 10+ years of experience in AI/ML, data science, or a similar field, with at least 5 years in a leadership role.
  • Proven track record of delivering AI solutions (particularly generative models) in complex, high-impact environments.
  • Experience managing cross-functional teams and large-scale, transformational projects.
• Technical Expertise
  • Proficiency in machine learning, deep learning, NLP, computer vision, and other AI domains.
  • Familiarity with the latest generative AI architectures (e.g., Transformer-based models, diffusion models, etc.) and large-language-model frameworks.
  • Experience with cloud-based AI platforms (AWS, Azure, GCP) and relevant ML frameworks (TensorFlow, PyTorch, etc.).
  • Demonstrated experience in building or integrating AI platforms in an enterprise setting.
• Business Acumen
  • Strong understanding of how AI can drive business value, with the ability to connect technical capabilities to strategic objectives.
  • Experience in budgeting, project planning, and managing vendor relationships.
  • Adept at quantifying business impact and ROI of AI initiatives.
• Leadership & Soft Skills
  • Exceptional communication skills, capable of translating complex technical topics into business language.
  • Demonstrated ability to lead, motivate, and mentor high-performing teams.
  • Strong problem-solving and decision-making abilities with a track record of managing ambiguity.
  • Collaborative mindset, capable of building consensus among diverse stakeholders.

• Strategic Thinking: Ability to set a clear vision for AI initiatives that align with overall business goals.
• Innovation & Creativity: Comfortable exploring novel approaches and challenging the status quo.
• Execution Excellence: Track record of taking AI concepts to production, with a focus on quality and scalability.
• Change Management: Skilled in driving organizational change, influencing culture shifts, and instilling a data-driven mindset.
• Risk Management: Adept at identifying and mitigating technical, ethical, and compliance-related risks.
• Collaboration & Influence: Strong interpersonal skills to forge partnerships with internal and external stakeholders.