Specialist: Ml Ops

**Core Competencies**:

- Extensive knowledge and experience with software development, web technologies, frameworks, concepts, and terms.
- Strong relational and non-relational database foundational knowledge.
- Experienced with analysis and improvement of business and software systems and processes.
- Strong DevOps, CI/CD, and cloud deployment best practises knowledge, understanding of standards and deployment options.
- Cloud platform management (GCP, Azure, AWS, etc.)
- Experience with Linux, JavaScript, Python 3, and tools in our stack including: Node.js, Flask, Django, FastAPI, React, TypeScript, Vite, Emotion, MUI, Mantine, Firebase, Google Analytics, Tableau, Alteryx, GitLab, Terraform, GCP, PowerPoint, Word & Excel.

**Responsibilities**:
**Machine Learning Engineering & Operations**:

- Architect, develop and maintain a declarative ML operations solution that enables the development, training, benchmarking, serving, and scaling of ML models in a Kubernetes based environment.
- Enhance our ML capabilities by using both open-source and internally developed tools, designed to efficiently serve and manage models through the modelling lifecycle.
- Contribute to the development of high-fidelity models and tools to support or simplify the serving of our models in production.
- Enable ML and workload orchestration by configuring and controlling systems that can scale horizontally using specialized tools and techniques.
- Maintain up-to-date knowledge of ML engineering platforms, tools, and related technologies.
- Guide standards and best practices across the data stack.**Processes, Automation and DevOps**
- Contribute to the development and maintenance of toolsets and frameworks used to automate the testing and benchmarking of our models.
Identify systemic inefficiencies, conceptualise possible solutions and drive their development.
- Ensure consistent documentation of all implemented tools, systems, and processes.
- Support tools, ML models and infrastructure lifecycles via standard service management principles and processes.
- **
Business and Leadership**
- Use service engineering and MLOps techniques to innovate and solve problems, translating business requirements into system designs.
- Engage with stakeholders to support the design and delivery of data science projects and solutions.
- Lead and develop a team of junior MLOps engineers.
- Contribute to our agile way of work and innovation culture.
**Required Certification/Professional Registration**:
Any data or cloud platform (GCP, Azure, AWS) certification is required, other relevant certifications will be highly advantageous

**Qualifications**:
A formal qualification of at least NQF level 6 in Computer Science, Mathematics, Statistics, Software and/or Machine Learning or a related field. Any relevant specialised certifications or a post-graduate degree will be especially advantageous.
**Experience**:
3-5 years relevant experience, of which at least 2 years must have been in a ML or data science environment. Experience in ICT / Telecommunications will be an advantage
**Special Requirements**:

- Experience with Kubernetes and any ML platform (Kubeflow, MLflow, Vertex AI).
- Experience with the modeling lifecycle, development, deployment, and continued training.
- Experience with one of more of the following frameworks: PyTorch, XGBoost, Scikit-learn, TensorFlow.
- Experience with Google Cloud Platform and its products.
- Comfortable with mono-repo architecture and the relevant tooling.
- Experience with git workflows and interfaces.
- Experience with semantic versioning.
**EXPERIENCE**:

- Do you have a formal qualification of at least an NQF level 6 in Computer Science, Mathematics,
- Statistics, Software and/ OR Machine Learning OR a relevant specialized certification
- or a post-graduate degree
- Do you have experience with Kubernetes and any ML platform (e.g., Kubeflow, MLflow, Vertex AI)
- Do you have experience with the modeling lifecycle, development, deployment and
- continued training
- Do you have experience with one of more of the following frameworks: PyTorch, XGBoost,
- Scikit-learn, TensorFlow
- Do you have experience with Google Cloud Platform, AWS, or Azure
- Do you have experience with Git Workflows and Interfaces

**EDUCATION**:

- Do you have a relevant 3-year qualification (minimum of NQF level 6) in Computer Science,
- Mathematics, Statistics, Machine Learning, or a related field
- Do you possess Data and cloud certifications such as (GCP, Azure, AWS)?