Specialist: Ml Ops

**Core Description**:
Support data driven solutions by leveraging your **service engineering and ML Ops **experience** **across a wide set of use cases within the Telkom Group and throughout its customer base. In this role, you will become a member of Telkom Strategic Insights (TSI), a new team within Telkom Group’s Strategic Division; focused on maturing Telkom’s data landscape by building data driven solutions for large-scale problems. To succeed, you should be highly skilled in **setting up and supporting a MLOps practice and framework with the ability to design, build and scale MLOps components and services for new and existing use cases across the group in a cloud environment. **TSI is one of the biggest data orientated teams in the country, with over 50 data professionals spread over 4 smaller teams: Data Management, Data Insights, AI/ML, and Engineering. This team structure enables us to achieve a holistic balance between start-up and corporate culture.

**Competencies**:

- Extensive knowledge and experience with ML engineering, operations, frameworks concepts, and terms.
- Strong relational and non-relational database foundational knowledge.
- Experienced with analysis and improvement of business and software systems and processes.
- Deep knowledge and understanding of best practices, standards and deployment options that enable the serving of models, on-prem and in the cloud.
- Cloud platform management (GCP, Azure, AWS, etc.)
- Experience with Linux, Python 3, SQL, JavaScript, and tools in our stack including: Docker, Kubernetes, Kubeflow, MLflow PyTorch, XGBoost, Scikit-learn, Keras, TensorFlow, Dask, Flask, Django, FastAPI, Vertex AI, GKE, Cloud Run, Ab Initio, Tableau, Alteryx, PowerBI, GitLab, Terraform, GCP, PowerPoint, Word & Excel.
- Communication (written and verbal)
- Stakeholder management
- Skilled planner
- Takes initiative
- Growth mindset
- Can do attitude
- Systems orientated
- Innovative thinker
- Works well in teams
- Works well under pressure
- Relishes dynamic/changing environment
- Hard working and conscientious
- Highly skilled in multitasking and context switching
- Excellent time management
- Can solve difficult problems
- Strives to achieve high quality output

**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 modeling 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**:
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