Job description

The AI Garage for Investments & Wealth is seeking a Director, AI / Machine Learning Software Engineer to provide senior technical leadership in establishing and operationalizing enterprise-grade AI Operations (AI Ops) capabilities. This role is critical to ensuring that AI applications, agentic solutions, models, data pipelines, and supporting platforms can be deployed, governed, monitored, and sustained in production with the reliability, security, and operational rigor required in a highly regulated financial services environment. This leader will serve as the bridge between Engineering and Production, providing oversight across the full AI lifecycle. The role is responsible for defining AI Ops standards, implementing automation and observability frameworks, establishing operational controls, and driving best practices for production support, resiliency, governance, and continuous improvement. Given the breadth of expertise required across cloud platforms, automation, observability, AI/ML operations, and production reliability, this position requires a senior technical leader with the experience and authority to define strategy, influence architecture decisions, and drive operational excellence across the AI Garage and the broader Wealth & Investment Management Technology organization. Key Responsibilities • Define and lead the AI Ops strategy for AI Garage for Investments & Wealth. • Establish standards, frameworks, and best practices for deploying, monitoring, and supporting AI/ML solutions in production. • Provide technical leadership across the full AI lifecycle, from development through deployment, governance, and ongoing operations. • Drive automation of AI/ML pipelines, model deployment, monitoring, alerting, and incident response processes. • Build and enhance observability frameworks to ensure visibility into model performance, data health, system reliability, and operational risks. • Partner with engineering, platform, infrastructure, data, risk, and production support teams to ensure AI solutions meet enterprise standards for resiliency, security, and compliance. • Establish operational controls and governance processes for AI applications, models, and data pipelines. • Lead efforts to improve production reliability, scalability, and efficiency for AI-powered applications and platforms. • Influence architecture decisions to ensure AI solutions are designed for operational excellence in a regulated environment. • Drive continuous improvement initiatives, including incident reviews, root cause analysis, and the adoption of lessons learned into engineering and operational practices. • Mentor and guide senior engineers and technical teams in AI Ops, SRE, and production support best practices. Required Qualifications • Bachelor’s degree in Computer Science, Engineering, or a related technical field; advanced degree preferred. • Significant experience in software engineering, machine learning engineering, AI/ML operations, or site reliability engineering. • Proven experience leading enterprise-scale AI/ML production operations in a highly regulated environment. • Strong expertise in cloud platforms, distributed systems, automation, CI/CD, and infrastructure as code. • Experience with observability, monitoring, logging, and alerting frameworks for complex production environments. • Deep understanding of AI/ML lifecycle management, including model deployment, monitoring, governance, and retraining processes. • Strong knowledge of production support, incident management, resiliency engineering, and operational controls. • Demonstrated ability to influence senior stakeholders, architecture decisions, and cross-functional technical strategy. • Excellent communication and leadership skills, with the ability to bridge engineering and production teams effectively. Preferred Qualifications • Experience within financial services or another highly regulated industry. • Familiarity with governance, risk, and compliance considerations for AI/ML applications. • Experience supporting agentic AI solutions, modern ML platforms, and large-scale data pipelines. • Knowledge of security, reliability, and performance engineering best practices for AI platforms. Why Join Us This is an opportunity to play a pivotal leadership role in shaping the AI operational backbone for Investments & Wealth. You will help define how AI solutions are reliably brought into production, managed, and scaled, while influencing enterprise-wide standards and advancing innovation in a critical growth area.

Requirements

Must-haves

Define and lead the AI Ops strategy for AI Garage for Investments & Wealth.
Establish standards, frameworks, and best practices for deploying, monitoring, and supporting AI/ML solutions in production.
Provide technical leadership across the full AI lifecycle, from development through deployment, governance, and ongoing operations.
Drive automation of AI/ML pipelines, model deployment, monitoring, alerting, and incident response processes.
Build and enhance observability frameworks to ensure visibility into model performance, data health, system reliability, and operational risks.
Partner with engineering, platform, infrastructure, data, risk, and production support teams to ensure AI solutions meet enterprise standards for resiliency, security, and compliance.
Establish operational controls and governance processes for AI applications, models, and data pipelines.
Lead efforts to improve production reliability, scalability, and efficiency for AI-powered applications and platforms.
Influence architecture decisions to ensure AI solutions are designed for operational excellence in a regulated environment.
Drive continuous improvement initiatives, including incident reviews, root cause analysis, and the adoption of lessons learned into engineering and operational practices.
Mentor and guide senior engineers and technical teams in AI Ops, SRE, and production support best practices.
Significant experience in software engineering, machine learning engineering, AI/ML operations, or site reliability engineering.
Proven experience leading enterprise-scale AI/ML production operations in a highly regulated environment.
Strong expertise in cloud platforms, distributed systems, automation, CI/CD, and infrastructure as code.
Experience with observability, monitoring, logging, and alerting frameworks for complex production environments.
Deep understanding of AI/ML lifecycle management, including model deployment, monitoring, governance, and retraining processes.
Strong knowledge of production support, incident management, resiliency engineering, and operational controls.
Demonstrated ability to influence senior stakeholders, architecture decisions, and cross-functional technical strategy.
Excellent communication and leadership skills, with the ability to bridge engineering and production teams effectively.

Nice-to-haves

Bachelor’s degree in Computer Science, Engineering, or a related technical field; advanced degree preferred.
Experience within financial services or another highly regulated industry.
Familiarity with governance, risk, and compliance considerations for AI/ML applications.
Experience supporting agentic AI solutions, modern ML platforms, and large-scale data pipelines.
Knowledge of security, reliability, and performance engineering best practices for AI platforms.

Application prep

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Keywords to mirror

aimachine learningmlci/cd

Platform: oracle-cloud
Effort: medium