Dar, the founding member of the Sidara group, is an international multidisciplinary consulting organization specializing in engineering, architecture, planning, environment, project management, facilities management, and economics. Sidara operates in 60 countries with 20,500 professionals, Dar connects people, places, and communities through innovative solutions to the world's most complex challenges. We deliver projects from inception through completion, embracing challenges to empower communities worldwide. Learn more at www.dar.com.
Our Vision and Values:
We aspire to be the chosen home of those with a gift for crafting solutions that empower people and an unwavering passion for learning and innovation. Our core values shape our culture and guide our decision-making. We are committed to:
- Excellence
- Responsibility
- Empowerment
- Connectivity
- Courage
About the Role:
We are seeking a Senior MLOps Engineer to design, deploy, and operate reliable, scalable ML and AI platforms for enterprise solutions. Reporting to the ML Lead, you will own production MLOps capabilities and collaborate with ML, Agentic AI, DevOps, and engineering teams to move models from experimentation into secure, monitored production environments.
This is a hands-on engineering role requiring strong software and cloud platform expertise, sound technical judgment, and practical experience supporting production machine learning systems.
Roles and Responsibilities:
MLOps Platform & Model Lifecycle
- Build and maintain ML pipelines for experimentation, training, versioning, deployment, and model lifecycle management.
- Develop reusable automation, CI/CD workflows, model registries, and deployment standards.
- Support reliable batch and real-time model serving across cloud and hybrid environments.
Production Infrastructure & Operations
- Deploy and operate scalable AI services using containers, Kubernetes, cloud infrastructure, and Infrastructure as Code.
- Implement monitoring, logging, tracing, model performance and drift detection, alerting, and incident response.
- Improve system availability, security, performance, and operational cost.
AI Platform Collaboration & Governance
- Provide shared serving, deployment, evaluation, and observability capabilities for ML and LLM-powered applications.
- Collaborate with Agentic AI teams on LLMOps and AgentOps platform needs, while application and agent logic remain with those teams.
- Apply security, access control, auditability, documentation, and engineering best practices; support technical reviews and mentoring.
Must Have:
- 8+ years of experience in software, platform, DevOps/SRE, data, or ML engineering, including hands-on production MLOps experience.
- Strong Python skills and experience with ML pipelines, model registries, model serving, and monitoring.
- Proficiency with Docker, Kubernetes, CI/CD, Git, and Infrastructure as Code.
- Experience with Azure, AWS, or GCP and ML platforms such as MLflow, Azure ML, SageMaker, Vertex AI, or equivalent.
- Understanding of ML workflows, model evaluation, drift, observability, security, and production troubleshooting.
- Strong collaboration, problem-solving, and technical communication skills.
Good to Have:
- Experience with LLMOps, AgentOps, model gateways, and AI evaluation/observability tooling.
- Experience with GPU infrastructure, distributed workloads, or optimized model inference.
- Experience with regulated, hybrid, or on-premises enterprise deployments.
What we are looking for:
A proactive senior engineer who takes ownership of production AI reliability, builds reusable platforms, and helps ML and Agentic AI teams deliver secure, maintainable solutions at scale.
Qualifications:
- Bachelor’s degree in Computer Science, Software Engineering, or a related field (or equivalent experience).
- 8+ years of relevant professional engineering experience with a track record of production ML deployments.
** While we carefully review all applications, only candidates meeting the specified requirements will be contacted for further consideration. We appreciate your understanding and thank all applicants for their interest.*
