Are you passionate about building the next generation of AI solutions? Join us to lead and mentor a team of talented engineers, drive innovation in generative and agentic AI, and deliver impactful, scalable technology for Risk Technology. You’ll collaborate with cross-functional partners and play a key role in shaping the future of Asset and Wealth Management Risk.

As a Lead Machine Learning Engineer – Agentic AI in Risk Technology, you will lead a specialized technical area, driving impact across teams, technologies, and projects. You will leverage your expertise in software engineering and multi-agent system design to deliver complex, high-impact initiatives. You will mentor and guide a team of engineers, foster best practices in ML engineering, and partner with data science, product, and business teams to deliver end-to-end solutions that drive value for the Risk business.

Job responsibilities:

Lead the deployment and scaling of advanced generative AI, agentic AI, and classical ML solutions for the Risk business.Design and execute enterprise-wide, reusable AI/ML frameworks and core infrastructure to accelerate AI solution development.Develop multi-agent systems for orchestration, agent-to-agent communication, memory, telemetry, and guardrails.Guide research on context and prompt engineering techniques to improve prompt-based model performance, utilizing libraries such as SmartSDK and LangGraph.Develop and maintain tools and frameworks for prompt-based agent evaluation, monitoring, and optimization at enterprise scale.Build and maintain data pipelines and processing workflows for scalable, efficient data consumption.Write secure, high-quality production code and conduct code reviews.Partner with Data Science, Product, and Business teams to identify requirements and develop solutions.Communicate technical concepts and results to both technical and non-technical stakeholders, including senior leadership.Provide technical leadership, mentorship, and guidance to junior engineers, promoting a culture of excellence and continuous learning.

Required qualifications, capabilities, and skills:

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.10+ years of experience in machine learning engineering.Strong proficiency in Python and experience deploying end-to-end pipelines on AWS.Hands-on experience in system design, application development, testing, and operational stability.Experience using LangGraph or SmartSDK for multi-agent orchestration.Experience with AWS and infrastructure-as-code tools such as Terraform.

Preferred qualifications, capabilities, and skills:

Strategic thinker with the ability to drive technical vision for business impact.Demonstrated leadership working with engineers, data scientists, and ML practitioners.Familiarity with MLOps practices, including CI/CD for ML, model monitoring, automated deployment, and ML pipelines.Experience with agentic telemetry and evaluation services.Hands-on experience building and maintaining user interfaces.