Lead DevOps Engineer, Foundry RnD Job Mastercard

IT Jobs, Mastercard Jobs.

We are seeking a Lead DevOps Engineer to join the Mastercard Foundry R&D team. You will help build and scale AI/ML infrastructure to support our innovation efforts, with a focus on automation, observability, and developer experience. The ideal candidate is hands-on, curious, motivated, and comfortable working in fast-moving R&D environments.

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What You’ll Do

• Drive Platform Infrastructure: Own DevOps and infrastructure for MLOps and agentic AI systems, establishing reusable patterns for CI/CD, scalable inference, orchestration, observability, and cost control. Design secure, scalable, repeatable systems using Infrastructure as Code (IaC) to support R&D workloads.
• Build secure CI/CD & automation systems: Enable secure tool access, workload isolation, and infrastructure for LLM-backed APIs and MCP servers, while partnering with security and compliance on access control, infrastructure governance and auditability.
• Ensure Reliability & Observability: Implement monitoring, logging, and alerting. Tune observability for ML-specific workloads to ensure performance, reliability, and operational insight.
• Provide Technical Leadership: Offer hands-on leadership across DevOps and platform initiatives. Review code, enforce best practices, improve tooling, and promote clean, well-tested infrastructure.
• Cross-Functional Collaboration: Partner with ML, software, and platform engineers to design deployment strategies, scope work, manage agile deliverables, and meet milestones.

What You’ll Bring

• Extensive DevOps Experience: 8–12+ years in DevOps, SRE, or platform engineering, including senior/lead roles. Experience designing end-to-end infrastructure systems, solving scale/performance challenges, and operating platforms in production.
• Cloud & Infrastructure Expertise: Strong skills in cloud platforms (AWS, Azure, or GCP) and AI/ML components such as Databricks, Azure ML, and MLflow. Deep experience with Infrastructure as Code using Terraform and orchestration tools like Terragrunt.
• Container & Orchestration Mastery: Expertise in Kubernetes and Docker, including how they optimise ML development workflows. Experience with container security, networking, and cluster management at scale.
• AI/ML Platform Knowledge: Understanding of ML workflow requirements—model registries, feature stores, AI agents, Retrieval-Augmented Generation (RAG) techniques, and frameworks like LangChain/LlamaIndex.
• Leadership & Mentorship: Ability to translate ambiguous goals into clear plans, guide engineers, and lead technical execution.
• Problem-Solving Mindset: Approach issues systematically, using analysis and data to select scalable, maintainable solutions.

Required Skills

• Education & Background: Bachelor’s degree in Computer Science, Engineering, or related field. 8–12+ years of proven experience architecting and operating production-grade infrastructure, especially those supporting AI/ML workloads.
• Infrastructure as Code: Expert in Terraform and IaC orchestration tools like Terragrunt. Strong experience with configuration management and GitOps practices.
• Programming & Scripting: Advanced Bash and Python skills and strong software engineering fundamentals (version control, CI, code reviews). Familiarity with Go or other systems programming languages is a plus.
• CI/CD & Automation: Hands-on experience with Jenkins, GitHub Actions, GitLab CI, or similar tools. Strong understanding of pipeline design, artifact management, and deployment strategies.
• Monitoring & Observability: Experience with monitoring stacks such as Prometheus, Grafana, Splunk, and ELK. Skilled in building dashboards, alerts, and tuning observability for ML-specific use cases.
• Cloud Infrastructure: Experience deploying systems on AWS/Azure/GCP. Familiar with cloud-native services, serverless computing, and managed Kubernetes offerings (EKS, AKS, GKE). Comfortable with Linux internals and shell scripting.
• Security & Networking: Knowledge of security best practices for MLOps, including data privacy, compliance, access controls, and encryption. Understanding of modern networking protocols (mTLS) and secure service communication.
• Collaboration & Agile Delivery: Strong communication skills and experience working with cross-functional teams. Ability to document designs clearly and deliver iteratively using agile practices.

Preferred Skills

• Databricks Experience: Hands-on experience with Databricks, including workspace administration, cluster management, Unity Catalog, Delta Lake, and Lakehouse architectures. Familiarity with Databricks workflows, jobs orchestration, and MLflow integration.
• Advanced Cloud & ML Platform Expertise: Experience with Azure ML, SageMaker, or similar ML platforms. Familiarity with model serving, feature stores, and ML pipeline orchestration.
• ML Frameworks Familiarity: Knowledge of ML frameworks like TensorFlow, PyTorch, or Scikit-learn to better support ML engineering teams.
• Enterprise Security: Experience working in complex enterprise environments with strict security and compliance requirements. Strong networking fundamentals, including configuring and maintaining secure mTLS-based communication between services.
• DevOps & Platform Innovation: Experience implementing self-service platform automation, developer portals, or internal developer platforms (IDPs).
• Continuous Learning: Motivation to explore emerging technologies, especially in AI, generative AI, and cloud-native infrastructure. Certifications, personal projects, or open-source contributions are a plus.

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How to Apply

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