Senior AI Engineer AI App Deployment MCP Containers

Job purpose

We are seeking experienced Senior AI Engineers who can independently deploy AI applications and

Model Context Protocol (MCP) servers into containerized production environments from day one. The

ideal candidate should have hands-on experience in deploying, managing, and optimizing AI/ML

applications in production with minimal onboarding.

Duties and Responsibilities

Key Responsibilities

AI Application Deployment

• Deploy and containerize AI/ML applications and MCP servers using Docker and Kubernetes.
• Manage production deployments, orchestration, scaling, monitoring, and troubleshooting of AI

workloads.

• Integrate AI applications and MCP tools with existing enterprise systems and infrastructure.
• Ensure secure, reliable, and high-performance deployment of AI solutions.
• Optimize AI application performance, resource utilization, and scalability.

Containerization & Infrastructure

• Design and maintain containerized deployment environments.
• Configure Kubernetes clusters and container orchestration platforms.
• Implement deployment best practices for high availability and disaster recovery.
• Collaborate with infrastructure and DevOps teams for production readiness.

AI Platform & Operations

• Advise engineering teams on AI deployment architecture, security, and operational best practices.
• Support production monitoring, logging, and incident resolution.
• Contribute to CI/CD pipelines for AI/ML applications.
• Ensure compliance with deployment standards and operational guidelines.

Qualifications

• Bachelor’s degree in Computer Science, Information Technology, Artificial Intelligence, or a related

field.

• 4 years of hands-on experience deploying AI/ML applications in production environments.
• Proven experience working independently on production deployments with minimal supervision.

Required Skills

• Strong hands-on experience deploying AI/ML applications.
• Expertise in Docker and Kubernetes (or equivalent container orchestration platforms).
• Strong knowledge of Model Context Protocol (MCP), including building, deploying, and integrating

MCP servers.

• Experience with production monitoring, troubleshooting, and performance optimization.
• Knowledge of container networking, security, and deployment strategies.
• Strong analytical and problem-solving skills.

Desired Qualifications

• Cloud deployment experience on AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Experience implementing CI/CD pipelines for AI/ML applications.
• Exposure to LLM orchestration and agentic AI frameworks.
• Knowledge of Infrastructure as Code (Terraform or similar tools).
• Familiarity with observability tools such as Prometheus, Grafana, or ELK Stack.