• Create technical content that engineers trust • Write blog posts, tutorials, and guides that come from real hands-on experience — not marketing fluff • Produce demo videos and screencasts that show the product working in realistic scenarios • Build technical case studies, white papers, and solution briefs that speak to both practitioners and decision-makers • Create and maintain demo apps, proof-of-concepts, and sandbox environments • Design reference architectures and implementation templates • Deliver technical presentations for webinars and events • Engage with DevOps and SRE communities where the real conversations happen • Gather feedback from technical users and bring it back to product and content strategy • Contribute to open-source initiatives and monitor what's moving in the industry • Collaborate with Marketing, Product, Engineering, and Sales to align content with what actually matters • Partner with Customer Success to surface real use cases and success stories • Support sales engineering with technical collateral that holds up under scrutiny

Role Description We are seeking a senior-level Artificial Intelligence and Machine Learning Operations Engineer to design, implement, and optimize scalable machine learning and artificial intelligence deployment pipelines that power real-world business impact. In this role, you will partner closely with Data Science, Data Engineering, and Analytics teams to ensure models are production-ready, performant, secure, and scalable across cloud platforms. You will automate and operationalize the full machine learning lifecycle, from data ingestion through retraining, while establishing best practices, governance frameworks, and enterprise standards for Artificial Intelligence and Machine Learning Operations. This is a highly visible individual contributor role that combines hands-on engineering with strategic influence across various initiatives. Qualifications - 5+ years of prior relevant experience in machine learning or artificial intelligence engineering, Data Science, or Analytics Engineering - Bachelor’s degree in Computer Science, Data Engineering, or related field required - Deep experience with machine learning and artificial intelligence pipeline development and full lifecycle automation - Demonstrated ability to provide technical leadership in Machine Learning Operations strategies and pipeline standardization - Proven impact on improving reliability, scalability, and efficiency of machine learning and artificial intelligence solutions - Strong proficiency in Python and PySpark - Experience designing and implementing CI/CD for machine learning workflows, including version control systems such as Git and DVC - Experience with monitoring, logging, drift detection, and automated retraining frameworks - Experience with cloud platforms such as Databricks, AWS, GCP, or Azure - Proficiency in containerization and orchestration including Docker and Kubernetes - Experience with orchestration and lifecycle management tools such as Airflow, Kubeflow, MLflow, or similar platforms - Experience guiding teams on artificial intelligence automation best practices - Experience supporting marketing, revenue, or operations analytics teams preferred - Familiarity with TensorFlow, PyTorch, Scikit-learn, or similar machine learning frameworks preferred Requirements - Design, build, and operate end-to-end machine learning and artificial intelligence pipelines supporting batch, streaming, and real-time inference use cases - Automate the full machine learning lifecycle including ingestion, feature engineering, training, validation, deployment, monitoring, and retraining - Implement CI/CD pipelines for machine learning systems with automated testing, validation gates, and controlled model promotion - Develop orchestration workflows using tools such as Airflow, Kubeflow, and MLflow for experiment tracking and governance - Optimize artificial intelligence workloads for performance, scalability, and cost efficiency using distributed compute and cloud-native services - Establish monitoring and observability frameworks including performance metrics, data quality checks, drift detection, and bias monitoring - Design automated retraining strategies including trigger-based, schedule-based, and performance-based refresh cycles - Create repeatable prompting frameworks and artificial intelligence guardrails to support safe and effective AI-assisted development - Implement access controls, secrets management, compliance standards, and security best practices across machine learning and artificial intelligence platforms - Evaluate and operationalize emerging artificial intelligence technologies and vendor tools, identifying measurable business value - Mentor engineers and data scientists on Machine Learning Operations best practices and influence enterprise-wide architectural standards Benefits - Prioritize your wellness, access programs crafted to nurture your mental and physical health. - Enjoy unbeatable discounts on hotel stays, dining, retail, entertainment, and exclusive partner perks for travel, tech, and beyond! - Savor delicious meals for free in our employee dining room. - Park with ease—whether you're on or off shift, it's free! - From healthcare to financial support and generous time-off options, we’ve got you covered. - Elevate your career with development programs, connect through networking events, and make a difference with community volunteer opportunities. Company Description The SHOW comes alive at MGM Resorts International. Have you ever wondered what it would be like to work in a place full of excitement, diversity, and entertainment? Are you enthusiastic about being a team player in one of the most fascinating industries in the world? At MGM Resorts, we seek individuals like YOU to create unique and show-stopping experiences for our guests.

• Lead, mentor, and coach a team of DevSecOps engineers. Conduct performance reviews, manage career development paths, and foster a culture of high performance. • Define the DevSecOps strategy and multi-quarter roadmap, aligning security initiatives with broader business and engineering goals. • Act as the primary point of contact for Engineering and Product leads to ensure security requirements are baked into the Product Discovery phase. • Lead the implementation of organizational guardrails using tools like OPA (Open Policy Agent) or Kyverno to ensure compliance is automated across all environments. • Oversee the design of secure cloud architectures (AWS/Azure/GCP) and Kubernetes clusters, focusing on Zero Trust networking and identity-driven access. • Evaluate, procure, and manage third-party security vendors and tools (e.g., Snyk, Wiz, Cloudflare), ensuring maximum ROI and technical fit. • Define and track key performance indicators (KPIs) such as Mean Time to Remediate (MTTR), vulnerability burn-down rates, and false-positive ratios. • Serve as a technical escalation point for high-severity security incidents and lead the "Blameless Post-Mortem" process to drive systemic improvements.

• Build secure platforms that power the future of digital innovation • Drive a future-proof DevSecOps and API ecosystem that accelerates delivery, strengthens security, and unlocks business value • Bring structure, speed, and security together in one powerful platform strategy • Set the direction and make it real across the organization • Define and drive a DevSecOps strategy that delivers secure, automated CI/CD pipelines at scale • Build and evolve API platforms, from gateways to developer portals, so teams can connect, innovate, and grow faster • Embed security by design into every step of the development lifecycle • Set clear governance and standards for API lifecycle management, including versioning, throttling, and analytics • Accelerate adoption of infrastructure as code, containerization, and orchestration to create resilient platforms • Monitor performance and security, act fast on risks, and continuously improve • Lead cloud-native and microservices transformations that future-proof the platform landscape • Guide and inspire engineers and specialists, building a culture of automation, ownership, and innovation • Explore new tools, challenge the status quo, and strengthen platform capabilities