As a senior member of our team, the Engineering Manager leads the engineers who build and deliver our clients' data and AI systems.
Locations (4)
Walnut Creek, California, United States
San Francisco, California, United States
Dallas, Texas, United States
Washington, United States
Compensation
This position is also eligible for bonus compensation.
Responsibilities
Lead one or more engineering teams through client engagements, turning data and AI strategy into working systems
Manage the full delivery lifecycle, from technical design and architecture decisions through implementation, code review, testing, and production hand-off
Hire, coach, and grow engineers, setting technical standards and building feedback loops, career paths, and mentorship
Partner directly with client engineering and technology leaders as a peer, translating ambiguous business requirements into a clear technical plan
Make and stand behind architecture and technology choices for data platforms and AI/ML systems, including cloud-native data lakes and warehouses, pipeline orchestration, and MLOps tooling
Requirements
Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
Lead design, development, and deployment of large language model (LLM) based AI capabilities for Mastercard’s products, ensuring scalability, performance, and cost efficiency.
Responsibilities
Design and own architecture for deploying and operating AI capabilities at scale
Develop high-performance Python backend services for LLM inference orchestration
Establish robust LLMOps and MLOps pipelines on Databricks (AWS) using MLflow
Apply traditional ML and statistical modeling in hybrid architectures alongside LLMs
Translate R&D outcomes into scalable production solutions
Requirements
Master's or Bachelor's degree in Computer Science, AI/ML, or Engineering
Leads IS&T's strategy and planning for artificial intelligence in research computing, coordinating infrastructure support for researchers and serving as the technical bridge between IT and the academic community.
Responsibilities
Set and drive the technical roadmap for AI-enabled research computing, including GPU/accelerator capacity planning, containerized and AI workflows, and MLOps tooling.
Anticipate emerging compute, data-pipeline, and model-serving needs and translate them into infrastructure plans.
Coordinate RCS staff daily work on research projects, including incident management and service request fulfillment.
Supervise, mentor, and develop the team executing this work; set priorities and manage service delivery.
Create annual, monthly, and ad hoc resource reports on system utilization and storage services.
Design and develop agentic AI use cases by integrating technologies such as LLMs, LangChain, vector databases, and enterprise AI orchestration frameworks into our core software platform.
Compensation
Competitive salary; Daily meal allowance of €8 per working day; Company car with fuel card or charging card; Hospitalization insurance; Group insurance
Responsibilities
Design and develop agentic AI use cases
Apply and extend protocols such as MCP and A2A
Deliver production-grade AI solutions
Build AI solutions that respect enterprise constraints
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