Job DescriptionThe Impact you will have in this role: The Systems Engineering family is responsible for the entire technical effort to evolve and verify solutions that satisfy client needs. The primary focus is centered on reducing risk and improving the efficiency, performance, stability, security, and quality of all systems and platforms. The Platform Hosting & Engineering role specializes in the development, implementation, and support of all distributed, mainframe, and network hosting services across the firm. Responsible for the installation, configuration, programming, and support of operating systems, complex networks, and distributed environments, deploying solutions to increase overall system reliability across the firm.
Your Primary Responsibilities: - Design, build, and maintain scalable AI/ML platforms using AWS services including SageMaker, Amazon Bedrock, Bedrock Agent Core, Amazon Q Developer and Kiro.
- Manage and optimize AWS EMR clusters for large-scale data processing, ensuring efficient resource utilization and high performance.
- Develop and maintain MLOps pipelines for model training, validation, and deployment.
Implement Amazon Bedrock capabilities including Guardrails and Knowledge Bases to enable secure use of foundation models. - Design and manage AI agent infrastructure using Bedrock Agent Core, including agent orchestration, memory, gateway, and runtime components for enterprise-scale deployments.
- Manage and maintain AWS Athena infrastructure, ensuring availability, performance, and proper configuration for analytical workloads.
- Implement cloud automation and infrastructure-as-code using Terraform, with CI/CD pipelines managed through Jenkins.
- Work with AWS services including VPC, Subnets, EC2, IAM, Security Groups, ECS, S3, ELB, Auto Scaling, Lambda, AWS Glue, Athena, EMR, and ADX to support platform and infrastructure operations.
- Configure SageMaker environments with network and security rules including VPC peering, endpoint creation, and security group configurations
Perform system upgrades and maintenance for distributed data platforms across multiple AWS accounts. - Implement centralized logging, monitoring, and compliance reporting for AI/ML systems
Collaborate with governance and risk teams to ensure compliance and approval of AI/ML models. - Enable secure data access and governance across accounts using data-sharing and access control mechanisms.
- Deploy machine learning models using AWS SageMaker, enabling predictive analytics and data-driven decision-making.
- Automate infrastructure provisioning using Terraform for SageMaker resources (KMS keys, S3 buckets, etc.).
- Configure cross-account integrations for Snowflake across all environments
Create customized CloudWatch alarms and configure log ingestion pipelines (CloudWatch to Splunk).
Qualifications: - Minimum of 4 years of related experience
- Bachelor's degree preferred or equivalent experience
Talents Needed for Success: - Fosters a culture where honesty and transparency are expected.
- Stays current on changes in his/her own specialist area and seeks out learning opportunities to ensure knowledge is up-to-date.
- Collaborates well within and across teams.
- Communicates openly with team members and others.
- Resolves disagreements between colleagues effectively, minimizing the impact on the wider team.
The salary range is indicative for roles at the same level within DTCC across all US locations. Actual salary is determined based on the role, location, individual experience, skills, and other considerations.
About the TeamServes as a dedicated technology resource for advancing DTCC's business opportunities and providing industry thought leadership for leveraging new technology. The goal of this new department is to partner internally with IT, our business and regulatory divisions and externally with clients, regulators, and fintech vendors, to help build new platforms and business models to advance DTCC's mission to support the financial markets.
