Senior Machine Learning Engineer - AI / GenAI at Datonomy Solutions

Datonomy Solutions; Emfuleni Local Municipality; Vor Ort; ZAR 1.000.000 - 2.000.000; Vollzeit

Zusammenfassung

Datonomy Solutions in South Africa seeks a Senior Machine Learning Engineer to design, build, deploy and support enterprise-scale AI/ML and GenAI solutions. The role focuses on productionising models, AI agents and RAG apps in Databricks and AKS environments.

You will collaborate with Data Scientists, Cloud and Platform teams, delivering scalable, secure and observable ML platforms with CI/CD, MLflow and model serving. Strong Python/SQL, MLOps, and cloud skills are required.

Qualifikationen

• Strong production experience delivering AI/ML in enterprise environments.
• Hands-on MLOps, DevOps practices in AI/ML contexts.
• Python, SQL and API development expertise.
• Experience with Databricks, MLflow and model serving.
• Kubernetes, Docker and container deployments on AKS.

Aufgaben

• Design, build, deploy and support production-grade ML/AI solutions.
• Productionise ML models and data pipelines using Databricks.
• Develop Generative AI apps, AI agents and RAG solutions.
• Create reusable ML pipelines with MLOps principles.
• Implement CI/CD, automated testing and model monitoring.
• Deploy ML workloads on AKS; develop REST APIs and microservices.
• Build containerised solutions with Docker/Kubernetes and ensure observability.

Kenntnisse

Python

SQL

Databricks

Kubernetes

Docker

AKS

MLflow

REST API

MLOps

Generative AI

RAG

LLMs

Spark

Ausbildung

Computer Science

Engineering

Econometrics

Mathematical Statistics

Actuarial Science

Tools

Databricks Workflows

Databricks Model Serving

Mosaic AI

MLflow

Azure Kubernetes Service (AKS)

Kubernetes

Docker

REST API Development

Jobbeschreibung

Role Overview

We are looking for an experienced Senior Machine Learning Engineer to design, build, deploy and support enterprise-scale Machine Learning, Artificial Intelligence and Generative AI solutions.

The role is strongly focused on the productionisation and operationalisation of AI and ML solutions, including machine learning models, GenAI applications, AI agents, Retrieval-Augmented Generation (RAG) solutions and reusable ML platforms.

The successful candidate will work extensively across Databricks, Azure Kubernetes Service (AKS), Kubernetes, Docker, MLflow and MLOps environments, partnering closely with Data Scientists, Cloud Engineers, Platform Teams, Security Teams and business stakeholders.

This is an engineering-focused role requiring strong experience taking AI and Machine Learning solutions from prototype through deployment, scaling, monitoring and production support.

Key Responsibilities

• Design, build, deploy and support production-grade Machine Learning and AI solutions.
• Productionise Machine Learning models and Data Science pipelines using Databricks.
• Develop and deploy Generative AI applications, AI agents and RAG solutions.
• Build reusable Machine Learning pipelines and frameworks using MLOps principles.
• Implement CI/CD, automated testing, model monitoring, governance and deployment automation.
• Deploy and optimise open-source Machine Learning and Large Language Models within Azure Kubernetes Service (AKS).
• Develop and support REST APIs and microservices that expose AI and Machine Learning capabilities to enterprise applications.
• Build scalable containerised solutions using Docker and Kubernetes.
• Implement and maintain Databricks Workflows, MLflow, Model Serving and Mosaic AI solutions.
• Monitor models for performance degradation, model drift, reliability and operational health.
• Troubleshoot production issues across models, ML pipelines, APIs, GenAI applications and supporting infrastructure.
• Optimise AI and ML platforms for performance, scalability, reliability and cost efficiency.
• Collaborate with Data Scientists to convert models and prototypes into business-ready production solutions.
• Partner with Cloud, Infrastructure, Security and Platform Engineering teams to ensure solutions comply with enterprise architecture and security standards.
• Contribute to reusable engineering frameworks, standards and best practices across the AI and Machine Learning ecosystem.
• Mentor junior engineers and support knowledge sharing within the engineering team.

Core Technical Requirements

Candidates Should Have Strong Hands-on Experience In

• Python
• SQL
• Databricks
• Databricks Workflows
• MLflow
• Databricks Model Serving
• Mosaic AI
• Microsoft Azure
• Azure Kubernetes Service (AKS)
• Kubernetes
• Docker
• REST API development
• Microservices
• CI/CD
• MLOps
• Machine Learning
• Generative AI
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• AI Agents
• Spark / distributed computing
• Cloud-native AI/ML platforms
• Monitoring and observability
• Infrastructure automation

Required Experience

• Strong experience building, deploying and supporting Machine Learning solutions in production environments.
• Proven experience with MLOps, DevOps and software engineering practices within AI/ML environments.
• Strong development experience using Python, together with SQL and API development.
• Hands-on experience with Databricks, MLflow and Model Serving.
• Strong experience with Kubernetes, Docker and containerised application deployment.
• Experience deploying Machine Learning and/or Generative AI workloads onto Azure Kubernetes Service (AKS).
• Experience implementing CI/CD pipelines, infrastructure automation and production monitoring.
• Experience with Spark or other distributed / large-scale data processing technologies.
• Practical understanding of LLMs, RAG architectures, Generative AI and AI agents.
• Experience taking AI or Machine Learning use cases from development through to production deployment and support.
• Experience working collaboratively with Data Scientists and engineering teams.
• Strong troubleshooting skills across applications, APIs, ML pipelines and cloud platforms.
• Ability to translate technical solutions into measurable business outcomes.

Qualifications

A relevant tertiary qualification in one of the following or a related discipline:

• Computer Science
• Engineering
• Econometrics
• Mathematical Statistics
• Actuarial Science

A Master's or Doctorate would be advantageous.

Preferred Certifications

• Microsoft Azure certifications such as AZ-104, AZ-305 or AI-102
• Databricks Data Engineer, Machine Learning Engineer or Generative AI Engineer
• Kubernetes certifications such as CKA or CKAD
• DevOps, MLOps or Platform Engineering certifications
• AWS or Google Cloud certifications
• Recognised Machine Learning, AI or Data Science certifications

Ideal Candidate Profile

This role would suit an experienced Machine Learning Engineer, MLOps Engineer, AI Engineer or GenAI Engineerwho combines strong software engineering capabilities with practical Machine Learning and cloud infrastructure experience.

The strongest candidates will have previously built and supported enterprise AI/ML platforms, rather than only developing models in notebook or research environments. They should be comfortable working across the full lifecycle from model development and experimentation through to APIs, containers, Kubernetes deployments, monitoring, governance and ongoing production support.

Desired Skills

• Senior Machine Learning Engineer
• ML Engineer
• Machine Learning Engineer