Senior Platform Engineer - AI Platforms & Deployment - Landmark

We are looking for the right people — people who want to innovate, achieve, grow and lead. We attract and retain the best talent by investing in our employees and empowering them to develop themselves and their careers. Experience the challenges, rewards and opportunity of working for one of the world’s largest providers of products and services to the global energy industry.
About the Role   You will be responsible for the deployment, operation, and reliability of next-generation AI-enabled software platforms. Our products combine modern web applications, cloud-native services, and agentic AI frameworks that coordinate multiple specialized agents and tools to automate complex geoscience workflows.
This role sits at the intersection of software engineering, infrastructure engineering, AI platform operations, and Dev Ops. You will help define how applications move from development to production, working closely with software engineers, AI engineers, visualization engineers, and architects to build secure, scalable, and repeatable deployment workflows.
You will design and maintain CI/CD pipelines, deployment automation, infrastructure-as-code, observability systems, and runtime environments that support both traditional enterprise applications and modern AI-driven systems.
You will build software for the following domain:
Geoscience - geology, geophysics, or petrophysics
Technologies used may include:
Type Script, Python, Java, C#, and Go
Angular, React, and Node.js
Azure OpenAI and other AI platforms
Agentic AI frameworks and orchestration platforms
MCP-based architectures and tool ecosystems
Azure and AWS cloud platforms
Kubernetes, Docker, Helm
Terraform, Ansible, Argo CD, Flux, Git Hub Actions, Azure Dev Ops Pipelines
PostgreSQL, SQL Server, MongoDB, Redis, Vector Databases
Prometheus, Grafana, Open Telemetry, ELK, Datadog, and other observability platform

Job Duties    Design and maintain CI/CD pipelines for web applications, services, and AI platforms
Build and operate Kubernetes-based environments across development, testing, and production
Develop Infrastructure-as-Code solutions that enable reliable and repeatable deployments
Design deployment architectures for cloud, hybrid, and on-premises environments
Deploy and operate AI-powered applications, agent runtimes, MCP servers, orchestration services, and supporting infrastructure
Implement monitoring, logging, tracing, alerting, and operational dashboards
Establish platform reliability, security, observability, and operational standards
Automate build, deployment, validation, and release processes
Troubleshoot and resolve production incidents across applications, infrastructure, and cloud environments
Apply security best practices and participate in architecture and security reviews
Qualifications    Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related discipline, or equivalent experience
7+ years of software engineering, Dev Ops, platform engineering, or site reliability engineering experience
Proven experience owning production deployments for business-critical applications
Deep experience with Kubernetes and containerized application deployment
Experience designing and maintaining enterprise CI/CD pipelines
Strong experience with Infrastructure-as-Code technologies such as Terraform, Ansible, or similar tools
Experience operating cloud-based applications in Azure, AWS, or similar platforms
Experience implementing monitoring, logging, alerting, and observability solutions

Experience with security, secrets management, identity integration, and RBAC
Experience deploying and operating AI-powered applications in production environments

Experience with LLM-based applications, AI agents, AI orchestration frameworks, vector databases, or similar technologies
Experience using AI-assisted development tools to improve infrastructure automation, deployment engineering, troubleshooting, and operational efficiency
Strong written and verbal communication skills
Preferred   Experience deploying multi-agent systems in production

Experience with AI orchestration frameworks such as Semantic Kernel, Lang Graph, CrewAI, Auto Gen, OpenAI Agents SDK, or similar technologies
Experience deploying and operating MCP-based systems and tool ecosystems

Experience with AI observability platforms such as Lang Smith, Lang Fuse, Arize, Phoenix, or similar tools
Experience operating hybrid cloud and on-premises environments
Experience mentoring less experienced engineers
Energy industry…