Senior Machine Learning Engineer - AI / GenAI

Datonomy Solutions (Pty) Ltd; Sandton; On-site; ZAR 1,800,000 - 2,600,000; Full time

Job summary

Datonomy Solutions (Pty) Ltd is seeking a Senior ML Engineer to design, build, deploy and support enterprise-scale ML/AI and Generative AI solutions. The role emphasises productionisation, operationalisation and scalable platforms across data science workflows.

The role collaborates with Data Scientists, Cloud Engineers and Platform teams, delivering production-grade models, GenAI applications, AI agents and RAG solutions using Databricks, AKS, Kubernetes, Docker and MLOps practices.

Qualifications

• Strong production-level ML in production environments.
• Experience with MLOps, DevOps and software engineering practices in AI/ML.
• Strong Python development with SQL and API work.
• Hands-on Databricks, MLflow and Model Serving experience.
• Kubernetes, Docker and containerised deployment expertise.
• Experience deploying on AKS and cloud platforms (Azure).
• CI/CD, infrastructure automation and production monitoring know-how.
• Familiarity with Spark or distributed data processing technologies.
• Understanding of LLMs, RAG architectures and AI agents.
• Experience taking AI/ML use cases to production and supporting them.

Responsibilities

• Design, build, deploy and support production-grade ML/AI solutions.
• Productionise ML models and pipelines using Databricks.
• Develop and deploy Generative AI apps, AI agents and RAG solutions.
• Build reusable ML pipelines and frameworks with MLOps principles.
• Implement CI/CD, automated testing, model monitoring, governance and deployment automation.
• Deploy and optimise ML/AI workloads on AKS and Docker/Kubernetes.
• Develop and support REST APIs and microservices exposing AI capabilities.
• Build scalable containerised solutions with Docker and Kubernetes.
• Implement Databricks Workflows, MLflow, Model Serving and Mosaic AI solutions.
• Monitor models for performance, drift, reliability and health.
• Troubleshoot production issues across models, pipelines and APIs.
• Improve performance, scalability, reliability and cost efficiency.
• Collaborate with Data Scientists to turn prototypes into business-ready solutions.
• Partner with Cloud, Infrastructure, Security and Platform teams for enterprise standards.
• Contribute to reusable engineering frameworks, standards and best practices.
• Mentor junior engineers and share knowledge within the team.

Skills

Python

SQL

Databricks

Databricks Workflows

MLflow

Databricks Model Serving

Mosaic AI

Azure

AKS

Kubernetes

Docker

REST APIs

Microservices

CI/CD

MLOps

Machine Learning

Generative AI

LLMs

RAG

AI Agents

Spark

Cloud-native AI

Monitoring

Infrastructure automation

Education

Computer Science

Engineering

Econometrics

Mathematical Statistics

Actuarial Science

Master's or Doctorate

Tools

Databricks

MLflow

AKS

Docker

Kubernetes

REST API frameworks

Databricks Workflows

Databricks Model Serving

Mosaic AI

Job description

Role Overview

We are looking for an experiencedSenior Machine Learning Engineerto design, build, deploy and support enterprise-scaleMachine Learning, Artificial Intelligence and Generative AI solutions.

The role is strongly focused on theproductionisation 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 acrossDatabricks, 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 fromprototype through deployment, scaling, monitoring and production support.

Key Responsibilities

• Design, build, deploy and supportproduction-grade Machine Learning and AI solutions.

• Productionise Machine Learning models and Data Science pipelines usingDatabricks.

• Develop and deployGenerative AI applications, AI agents and RAG solutions.

• Build reusable Machine Learning pipelines and frameworks usingMLOps principles.

• ImplementCI/CD, automated testing, model monitoring, governance and deployment automation.

• Deploy and optimiseopen-source Machine Learning and Large Language ModelswithinAzure Kubernetes Service (AKS).

• Develop and supportREST APIs and microservicesthat expose AI and Machine Learning capabilities to enterprise applications.

• Build scalable containerised solutions usingDocker and Kubernetes.

• Implement and maintainDatabricks Workflows, MLflow, Model Serving and Mosaic AIsolutions.

• Monitor models forperformance 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 forperformance, scalability, reliability and cost efficiency.

• Collaborate with Data Scientists to convert models and prototypes intobusiness-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

The ideal candidate will have:

• Strong experience building, deploying and supportingMachine Learning solutions in production environments.

• Proven experience withMLOps, DevOps and software engineering practiceswithin AI/ML environments.

• Strong development experience usingPython, together with SQL and API development.

• Hands-on experience withDatabricks, MLflow and Model Serving.

• Strong experience withKubernetes, Docker and containerised application deployment.

• Experience deploying Machine Learning and/or Generative AI workloads ontoAzure Kubernetes Service (AKS).

• Experience implementingCI/CD pipelines, infrastructure automation and production monitoring.

• Experience withSpark or other distributed / large-scale data processing technologies.

• Practical understanding ofLLMs, RAG architectures, Generative AI and AI agents.

• Experience taking AI or Machine Learning use cases fromdevelopment through to production deployment and support.

• Experience working collaboratively withData Scientists and engineering teams.

• Strong troubleshooting skills across applications, APIs, ML pipelines and cloud platforms.

• Ability to translate technical solutions into measurablebusiness outcomes.

Qualifications

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

• Computer Science

• Engineering

• Econometrics

• Mathematical Statistics

• Actuarial Science

AMaster's or Doctoratewould be advantageous.

Preferred Certifications

Relevant certifications would be beneficial, including:

• Microsoft Azure certifications such asAZ-104, AZ-305 or AI-102

• DatabricksData Engineer, Machine Learning Engineer or Generative AI Engineer

• Kubernetes certifications such asCKA 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 experiencedMachine 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 supportedenterprise 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.