The MLOps Engineer is a key player in operationalizing machine learning workflows, ensuring the seamless transition of models from development to production while optimizing scalability, reliability, and performance. This role involves designing and maintaining robust pipelines for deploying, monitoring, and managing machine learning models in production environments, automating workflows, and integrating AI solutions with existing systems. Collaborating with data scientists, DevOps, and software teams, the MLOps Engineer ensures models remain performant, secure, and compliant throughout their lifecycle. By leveraging cutting-edge tools, implementing best practices, and addressing challenges like model drift and data inconsistencies, this role drives innovation and maximizes the business impact of AI solutions.
Required Skills
Python, C++, Java, TensorFlow, PyTorch, Scikit-learn, Apache Spark, Hadoop, AWS, GCP, Azure, Docker, Kubernetes, MLflow, Git, GitHub, GitLab CI/CD, Terraform, CloudFormation, Prometheus, Grafana, SageMaker, Vertex AI, Azure ML, Reinforcement Learning, NLP, Computer Vision, Edge AI.
Preferred Qualifications:
- Strong knowledge of distributed systems and parallel computing architectures for large-scale machine learning applications.
- Experience with edge computing and developing machine learning solutions for IoT (Internet of Things) devices.
- Familiarity with working in regulated environments, understanding compliance, security protocols, and industry standards (e.g., GDPR, HIPAA).
- Associate-level cloud certification, such as AWS Certified Machine Learning - Specialty, Microsoft Certified: Azure AI Engineer Associate, or Google Cloud Professional Machine Learning Engineer.
- Experience with real-time data processing and streaming platforms, such as Apache Kafka or Apache Flink, for AI-driven applications.
- Understanding of AI model deployment and monitoring in production systems with a focus on scalability and optimization.
- Familiarity with hybrid cloud environments and multi-cloud strategies to deploy AI workloads across various platforms.
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- 7+ years of relevant hands-on experience
- 5+ years experience with Docker and Kubernetes, provisioning production clusters and maintaining their compliance.
- 5+ years experience supporting enterprise Cloud applications or infrastructure (AWS, Azure, etc.)
- Solid understanding of Helm Charts
- Practical experience with Machine Learning on Kubernetes
- Experience managing clusters with GPU machines
- Experience building and maintaining machine learning platforms and pipelines
- Practical programming and scripting skills (Python preferred)
- Fast learner, analytical thinker, creative, hands-on, strong communication skills
- Able to work both independently and as part of a team
- Excellent problem-solving skills and attention to detail.
- Proven experience with modern software development and engineering practices including scrum/agile, Git, and DevOps
- Experience with or managing KubeFlow deployments
- Knowledge of Istio
- Comfortable provisioning and debugging complex CI/CD pipelines
- Prior experience with Terraform
- Publications or GitHub repos showcasing your skills
- Experience with TypeScript
- Prior work with LLM’s and Agentic Workflows
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Key Responsibilities:
- Design, develop, and maintain scalable ML pipelines for deploying machine learning models into production environments.
- Collaborate with data scientists and software engineers to translate research models into production-ready applications.
- Build and manage CI/CD pipelines tailored for machine learning workflows, ensuring rapid and reliable deployment.
- Implement monitoring solutions to track model performance, detect drift, and manage system health.
- Automate data ingestion, preprocessing, and feature engineering pipelines for consistent and reliable input data.
- Manage cloud-based and on-premise infrastructure using orchestration tools like Kubernetes and Terraform.
Job Type: Full-time
Pay: ₦1,220,000.00 per month
Application Question(s):
- What is the most impressive job you’ve done in the past with machine learning?
- How long have you been working with python?
Application Deadline: 30/09/2025