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Vacancy
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Job Description
Roles & Responsibilities
Key Responsibilities
CI/CD & Automation Engineering
- Design, build, and maintain CI/CD pipelines for AI models, LLM services, and software applications.
- Automate build, test, deployment, and environment configuration workflows to enable rapid and reliable releases.
AI & LLM Deployment Operations
- Deploy, operate, and scale AI systems, LLM APIs, inference workloads, and cloud-based AI services.
- Ensure high availability, horizontal scalability, and low-latency inference across all production environments.
Infrastructure, Reliability & Cost Optimization
- Monitor infrastructure performance, system health, and AI workloads using observability and monitoring tools.
- Optimize infrastructure for reliability, performance, and cloud cost efficiency.
Security, Compliance & Governance
- Implement and enforce security best practices, access controls, secrets management, and environment isolation.
- Ensure infrastructure and deployment processes align with national data governance, compliance, and cybersecurity standards.
Cross-Functional Enablement
- Collaborate closely with AI Engineers, Full-Stack Engineers, and Product teams to enable seamless, scalable deployments.
- Act as the primary technical owner for production reliability during mission-critical deployments.
Documentation & Architecture Standards
- Maintain comprehensive documentation for DevOps workflows, system architecture, environments, and deployment standards.
- Ensure operational readiness, auditability, and knowledge transfer across teams.
Desired Candidate Profile
Minimum 5 years of hands-on DevOps engineering experience in production environments.
Mandatory: Proven experience deploying and operating AI systems and LLM-based workloads in production.
Strong hands-on expertise with Docker, Kubernetes, CI/CD platforms, and cloud services.
Experience with monitoring, observability, logging, and infrastructure-as-code (e.g., Terraform, similar tools).
Strong understanding of networking, security, and cloud-native architecture principles.
Excellent troubleshooting and incident response capabilities in high-availability systems.
Preferred Qualifications
- Experience with MLOps platforms such as MLflow, SageMaker, Vertex AI, or similar.
- Proven experience scaling AI and LLM applications in high-traffic production environments.
- Exposure to AI model lifecycle management, retraining pipelines, and operational governance.
- Experience in government, regulated, or national-scale enterprise environments.
Company Industry
- Education
- Training
- Teaching
- Academics
Department / Functional Area
- IT Software
Keywords
- DevOps Engineer
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