Senior ML Engineer: GenAI, MLOps & Production AI

Praesignis (Pty) Ltd; Gauteng; On-site; ZAR 1,000,000 - 1,700,000; Full time

Job summary

Praesignis (Pty) Ltd is seeking a Senior ML Engineer with hands-on experience in productionising and scaling ML, AI, and Generative AI solutions. The role emphasizes strong software/cloud engineering, Databricks, MLflow, AKS, Python, Docker, Kubernetes and MLOps.

You will take ML prototypes to production, build AI Agents, GenAI and RAG apps, deploy open-source LLMs, develop APIs and microservices, and ensure scalable, secure, monitored, and reliable platforms across the stack.

Qualifications

• Experience productionising ML/DS solutions at scale.
• Strong software/cloud engineering background in Databricks, AKS and MLOps.
• Proven ability to deploy and monitor AI/GenAI solutions in production.

Responsibilities

• Productionise, deploy and monitor ML models and pipelines on Databricks.
• Build AI Agents, GenAI and RAG solutions on Databricks.
• Develop reusable ML pipelines with CI/CD, testing, monitoring and governance.
• Deploy and manage AI/ML models on AKS.
• Design APIs and microservices on AKS to expose capabilities.
• Containerise solutions with Docker and Kubernetes for scalable deployments.
• Monitor performance, drift and operational health in production.
• Collaborate with Data Scientists to productionise prototypes.
• Ensure compliance with enterprise standards and security requirements.
• Troubleshoot production issues in models, pipelines, APIs and apps.
• Drive performance, cost efficiency and reliability improvements.

Skills

Databricks workflows

MLflow

AKS

Python

SQL

REST APIs

Docker

Kubernetes

CI/CD

MLOps

GenAI / LLM deployment

Cloud engineering

Monitoring & observability

Education

Masters or Doctorate in Computer Science, Engineering, Econometrics, Mathematical Statistics or Actuary Science

Tools

Databricks

MLflow

AKS

Docker

Kubernetes

REST APIs

Job description

Praesignis (Pty) Ltd is seeking a Senior ML Engineer with hands-on experience in productionising and scaling ML, AI, and Generative AI solutions. The role emphasizes strong software/cloud engineering, Databricks, MLflow, AKS, Python, Docker, Kubernetes and MLOps.

You will take ML prototypes to production, build AI Agents, GenAI and RAG apps, deploy open-source LLMs, develop APIs and microservices, and ensure scalable, secure, monitored, and reliable platforms across the stack.