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We are partnering with a leading firm in Abu Dhabi, actively seeking an experienced MLOps Engineer to join our team. As an MLOps Engineer, you will be responsible for building and implementing ML systems that leverage generative models for various applications. You will work closely with our client's data scientists and software engineers to develop scalable and robust ML pipelines.
1. Develop and maintain ML pipelines: Design, build, and optimize workflows that support models from development to deployment. Implement best practices for building and versioning ML models for seamless integration into production systems.
2. Infrastructure management: Collaborate with infrastructure and DevOps teams to provision and maintain the necessary computing resources, including GPUs, clusters, and cloud services, to support generative models.
3. Model development and deployment: Collaborate with data scientists to develop, Implement model training, evaluation, and performance tracking techniques. Deploy models into production systems, ensuring scalability, efficiency, and reliability.
4. Monitoring and debugging: Set up monitoring systems to track model performance and detect anomalies. Troubleshoot and resolve issues related to model performance, data quality, and infrastructure bottlenecks.
5. Collaboration and documentation: Collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to understand requirements and deliver ML solutions. Document the developed systems, processes, and infrastructure for future reference.
6. Stay updated on emerging trends: Continuously research, explore, and evaluate the latest advancements in generative AI, ML, and MLOps technologies. Propose innovative ideas and improvements to enhance the efficiency and effectiveness of our ML systems.
Desired Qualifications and Skills
Proven experience as a DevOps Engineer with a focus on MLOps.
Strong knowledge of CI/CD tools (with Linux, networking)
Good understanding of security concerns and basic security concepts.
Experience with ML lifecycle tools like MLflow, Kubeflow, Seldon, or TFX.
Proficiency in programming languages like Python and/or R.
Experience with cloud platforms like AWS, GCP, or Azure.
Knowledge of data versioning tools like DVC and ML experiment tracking systems.
Experience with Docker containers, Kubernetes, AWS EKS, helm charts,
Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.)
Strong understanding of ML model deployment strategies and monitoring ML models in production.
Excellent problem-solving skills and the ability to work collaboratively in a fast-paced environment.
Excellent written and verbal communication skills.
Fast learner, independent and with a proven ability to multitask on several projects at a time.
Ability to identify inefficient processes, and to be proactive in working towards improving them.
Familiarity with MLops pipelines
Familiarity with statistical modeling techniques - an advantage.
If you find these qualifications align with your expertise and aspirations, then please submit your CV today to apply for this opportun