AVP/VP, AI Engineer, Data Management Office at SMBC

, , Singapore -

Full Time

Start Date

Immediate

Expiry Date

08 Jul, 26

Salary

0.0

Posted On

09 Apr, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Engineering, Machine Learning, LLM Architectures, Agentic AI, Python, PySpark, PyTorch, TensorFlow, Azure, Azure Databricks, Model Evaluation, API Integration, Workflow Orchestration, Responsible AI, Data Governance, Vector Databases

Industry

Financial Services

Description

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.

Responsibilities

Design, build, and validate agentic AI workflows while ensuring robustness, safety, and compliance with regulatory standards. Collaborate with cross-functional teams including data science, engineering, and risk to deploy AI solutions effectively.