AVP/VP, AI Engineer, Data Management Office
At a glance
VP-level AI Engineering role at Sumitomo Mitsui Banking Corporation. Singapore.
Pay not stated AI Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and check agent workflows that handle multi-step reasoning and coordinate tool use
- Trace model and agent decisions to find safety, reliability and error-propagation issues
- Connect agents to APIs, plugins, external tools and the systems they need to operate
- Create LLM and agent tests, benchmarks and automated evaluation processes
- Apply responsible-AI controls and work with data, engineering, architecture and risk teams on compliant deployments
What you bring
- At least three years in AI/ML engineering, model development, evaluation or a related technical role
- Working knowledge of machine learning, LLM design and the components behind agent-based systems
- Experience testing LLMs and agents with scenarios, automated checks and diagnostic tracing
- Proficiency in Python, especially PySpark and MLlib, plus Hadoop and modern AI/ML frameworks
Who this fits
This role suits an experienced AI/ML engineer who can evaluate agent behaviour and work across technical and risk teams. The position is based in Singapore.
From the employer
Headquartered in Tokyo, Sumitomo Mitsui Banking Corporation (SMBC) is a leading global financial institution and a core member of Sumitomo Mitsui Financial Group (SMBC Group). Built upon our rich Japanese heritage since 1876, we put our customers first and provide seamless access to, from and within the Asia Pacific region. SMBC is one of the largest Japanese banks by assets and maintain strong credit ratings across our global integrated network. We work closely as one SMBC Group to offer personal, corporate and investment banking services to meet the needs of our customers.
With sustainability embedded within our strategy and operations, we are committed to creating a society in which today’s generation can enjoy economic prosperity and well-being, and pass it on to future generations.
Responsibilities
• Design, build and validate agentic AI workflows, including multi‑step reasoning and tool‑use orchestration.
• Analyse agent and model behaviour to assess robustness, safety, error propagation and end‑to‑end decision quality.
• Integrate APIs, external tools, plugins and system connectors required for agent operations.
• Develop and execute evaluation approaches for LLMs and agentic systems, including scenario tests, benchmarks and automated pipelines.
• Assess retrieval components, vector databases, memory systems and workflow reliability.
• Apply Responsible AI principles and regulatory expectations to model behaviour, documentation and control standards.
• Partner with data science, engineering, architecture and risk teams to ensure safe and compliant AI deployment.
• Collaborate with other departments’ data scientists to understand modelling intent, technical assumptions and workflow logic.
• Contribute to AI/ML proof‑of‑concept (POC) initiatives to strengthen evaluation practices and support innovation.
Requirements
• Strong understanding of machine learning and LLM architectures, including components used in agentic AI systems.
• Minimum 3 years of relevant experience in AI/ML engineering, model development, model evaluation, or related technical roles.
• Knowledge of multi‑step reasoning, planning loops, workflow orchestration and tool‑use frameworks.
• Experience evaluating LLMs, agents and AI systems through scenario‑based testing, automated evaluations and diagnostic tracing.
• Ability to assess robustness, stability, drift, failure recovery behaviours and unintended model actions.
• Hands‑on experience with API integration, workflow automation and inter‑system tool‑use; familiarity with Power Automate, Power Apps, Sharepoint is a plus.
• Understanding of AI governance frameworks, lifecycle controls, Responsible AI principles and regulatory expectations.
• Strong prompting, debugging, observability and tracing skills to diagnose complex multi‑turn agent behaviours.
• Ability to collaborate across technical, risk, governance and business teams effectively.
• Proficiency in Python (especially pyspark, MLlib), Hadoop stack and modern AI/ML frameworks such as PyTorch, TensorFlow, Keras, scikit-learn; familiarity with ML lifecycle framework/tool such as MLflow; familiarity with RAG, vector databases, retrieval systems and orchestration libraries is a plus.
• Familiarity with Copilot Studio is a huge plus.
• Strong familiarity with Microsoft Azure, Azure Databricks and Microsoft Purview is a huge plus.