DescriptionAre 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. Youll 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:
- Bachelors or Masters 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.
Required Experience:
Exec
DescriptionAre 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. Youll collaborate with cross-functional partners and p...
DescriptionAre 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. Youll 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:
- Bachelors or Masters 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.
Required Experience:
Exec
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