Lead Cloud Engineer

Lead Cloud Engineer P990

OVERVIEW:
This role will operate within our technology organization, working across multiple product areas and business domains. The Senior Engineer will play a critical role in shaping technical architecture, driving modernization efforts, and enabling cross-functional collaboration across teams. With a focus on cloud engineering (Azure), Databricks, API development, Snowflake, and artificial intelligence/machine learning (AI/ML), this role will help set the technical direction for the organization while providing hands-on implementation and mentorship.

The ideal candidate is both a strategic thinker and a doer, capable of diving deep into legacy systems, modernizing them, and driving adoption of new technologies and practices across the organization. Additionally, this role will focus on scaling AI/ML capabilities across the enterprise, developing proof of concepts (POCs), and enabling teams to leverage machine learning for impactful use cases.

This role will require working across the stack — from understanding legacy components to building entirely new solutions with organization-wide impact. A key focus will be enabling migrations to the cloud, scaling AI/ML models, and supporting “day 2” operations with a customer-enablement mindset. The goal is to empower teams to solve their challenges using common tooling and shared solutions, while reducing duplication of effort and driving enterprise-wide efficiency.

RESPONSIBILITIES:

• Technical Leadership:
• Design, develop, test, implement, and support scalable technical architectures across cloud, data, AI/ML, and API domains.
• Discover common underlying patterns and provide technical guidance and documentation for best practices (e.g., CI/CD, monitoring, logging, API development, data pipelines, AI/ML pipelines).
• Act as a thought leader and subject matter expert across multiple teams and domains, ensuring alignment with organizational goals.
• Cloud, Data, and AI Modernization:
• Lead efforts to modernize legacy systems and shape the organization's transition to cloud-native solutions, particularly on Azure.
• Build and maintain integrations with Snowflake, Databricks, and other data platforms, enabling seamless data sharing and insights.
• Develop and deploy scalable APIs and microservices that support cross-functional business needs while ensuring maintainability.
• Design and implement scalable AI/ML pipelines and workflows to support enterprise-wide AI initiatives.
• AI/ML Enablement & POCs:
• Partner with data science teams and business stakeholders to identify AI/ML use cases and develop proof of concepts (POCs) to validate their feasibility and impact.
• Scale successful AI/ML POCs into production-grade solutions, ensuring reliability, performance, and maintainability.
• Collaborate with teams to implement MLOps best practices for model deployment, monitoring, and governance.
• Identify opportunities to integrate AI/ML into existing systems and workflows to drive automation and insights.
• Mentorship & Enablement:
• Mentor team members and cross-train engineers across the organization to elevate technical skills and adoption of modern practices.
• Conduct internal training sessions and showcase new technologies, including AI/ML advancements, to drive adoption and understanding.
• Proof of Concept & Implementation:
• Work with teams to design and execute proof of concepts for new technologies and tools, with the goal of scaling successful solutions across the organization.
• Evaluate and implement automation tools (e.g., Terraform, Helm) and cloud-native services to improve efficiency and reliability.
• Incident Management & Continuous Improvement:
• Troubleshoot large-scale incidents alongside other technology teams, driving resolution and identifying opportunities for prevention.
• Participate in post-incident retrospectives and implement improvements to reduce future risks.
• Standardization & Tooling Optimization:
• Drive adoption of common tooling and reduce duplication of effort across teams by standardizing processes and technologies.
• Incorporate feedback from users and stakeholders to continuously improve platform services, AI/ML pipelines, and shared solutions.

QUALIFICATIONS, SKILLS, AND EXPERIENCE:

• Education & Work Experience:
• Bachelor’s degree in IT, Computer Science, or related field
• Proven track record of working across cloud engineering, data platforms, AI/ML, and API development in a senior technical capacity.
• Technical Expertise:
• Deep understanding of modern software deployment and architecture patterns, including containerization, CI/CD pipelines, and monitoring/logging.
• Strong experience with Azure cloud services, including resource provisioning, networking, and cost optimization.
• Hands-on experience with Databricks, Snowflake, and building scalable data pipelines and integrations.
• Expertise in API design and development, including RESTful and event-driven architectures.
• Experience in designing, deploying, and scaling AI/ML models in production environments.
• Familiarity with MLOps practices, tools, and frameworks for managing the machine learning lifecycle.
• Hands-on experience with automation and IaC tools like Terraform and Helm, as well as orchestration platforms.
• AI/ML Experience:
• Strong understanding of AI/ML concepts, frameworks, and tools (e.g., TensorFlow, PyTorch, MLflow, Azure Machine Learning).
• Experience in building and deploying AI/ML models at scale, including data preprocessing, training, and model monitoring.
• Ability to work with data scientists and translate business problems into AI/ML solutions.
• Cross-Functional Collaboration:
• Ability to work across teams and domains, serving as a bridge between engineering, data, AI, and business teams.
• Strong interpersonal and communication skills to mentor, document, and present technical solutions effectively.
• Problem Solving & Innovation:
• Ability to dive deeply into complex systems, identify opportunities for improvement, and drive modernization efforts.
• Passion for staying up-to-date on technology trends, including AI/ML advancements, and applying them thoughtfully to solve organizational challenges.
• Certifications & Tools:
• Certifications in relevant technologies (e.g., Azure Architect, Databricks Certified Developer, Kubernetes Administrator, Azure AI Engineer) are a plus.
• Familiarity with tools such as GitHub Actions, Datadog, Dynatrace, Grafana, Prometheus, and ServiceNow preferred.

TECHNOLOGIES IN USE (FOR REFERENCE):

• Cloud: Azure (preferred), AWS, GCP
• Data: Snowflake, Databricks, Azure Data Factory
• AI/ML: Azure Machine Learning, MLflow, TensorFlow, PyTorch
• Automation: Terraform, Helm
• Monitoring & Logging: Datadog, Grafana, Prometheus, Dynatrace
• CI/CD & DevOps: GitHub Actions, Harness CD, Artifactory

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