MLOps Engineer (Python SQL) (CPT)

Datafin; Cape Town; On-site; ZAR 800,000 - 1,200,000; Full time

Job summary

A reputable Independent Asset Management Firm based in Cape Town is seeking an experienced MLOps Engineer. The ideal candidate will have over 5 years of practical experience in delivering ML projects from concept to production, with strong skills in Python and SQL. You'll be designing MLOps infrastructure, leading full-lifecycle ML projects, and mentoring junior team members while promoting best practices in data science. The role emphasizes innovation, collaboration, and the drive to continuously improve data science culture across the organization.

Qualifications

• 5+ years delivering commercially driven ML projects from concept to production.
• Strong grasp of data warehousing and modelling concepts.
• Ability to build meaningful relationships and work in cross-functional teams.

Responsibilities

• Architect and automate end-to-end ML pipelines in Azure or DataBricks.
• Lead full-lifecycle ML projects from ideation to monitoring.
• Translate business questions into data-science roadmaps.
• Embed security and efficiency into the development process.

Skills

Expert Python

Solid SQL

Modern development practices (Git, tests, code review, CI/CD)

Hands-on design of MLOps pipelines

Collaboration with Data Engineering

Familiarity with Deep-Learning frameworks (TensorFlow/PyTorch)

Vector databases knowledge

Passion for upskilling others

Excellent communication skills

Tools

Docker

Kubernetes

Azure

DataBricks

Mosaic

BigQuery

Spark

Kafka

dbt

Job description

MLOps Engineer (Python & SQL) (CPT)IT - Software Development

Cape Town - Western Cape - South Africa

ENVIRONMENT

DELIVER Data-Science value discover, build, train and deploy ML solutions that drive measurable value and impact as the next MLOps Engineer wanted by a reputable Independent Asset Management Firm. You will design the MLOps infrastructure that lets models move from notebook to production safely, quickly and cost‑effectively. Providing thought leadership & radical innovation you will also challenge conventional approaches, champion out‑of‑the‑box thinking and push the team to explore bold, future‑focused ideas that keep the firm ahead of the curve. The successful incumbent will require 5+ years delivering commercially driven ML projects from concept to production, expert Python and solid SQL; modern development practices (Git, tests, code review, CI/CD) with hands‑on experience designing and operating MLOps pipelines: containerisation (Docker/K8s), automated deployment, drift monitoring & retraining triggers.

DUTIES

MLOps & Engineering

• Architect and automate end‑to‑end ML pipelines (CI/CD, testing, monitoring, retraining) in Azure, DataBricks or Mosaic.
• Build feature stores, model registries and scalable batch/streaming data pipelines (BigQuery/Spark/Kafka/dbt).

Model Development

• Lead full‑lifecycle ML projects ideation, experimentation, production and post‑deployment monitoring.
• Apply regression, classification, clustering, time‑series forecasting and recommender systems to real‑world retail problems.

Innovation

• Track emerging research (LLMs, ensemble methods, vector search) and run PoCs to keep the stack ahead of the curve.
• Demonstrate thought leadership by challenging the status quo, encouraging radical, out‑of‑the‑box thinking and inspiring the team to step outside their comfort zone.
• Drive a culture of continuous experimentation and learning to unlock innovative solutions.

Stakeholder Engagement

• Translate fuzzy business questions into data‑science roadmaps, articulate complex concepts to non‑technical partners.
• Coach and mentor Analysts/Engineers, promoting best‑practice coding and MLOps approaches.

Governance & Efficiency

• Embed security, cost‑optimisation and observability into everything you build.
• Define operating procedures, documentation and design patterns that future‑proof the platform.

REQUIREMENTS

• 5+ Years delivering commercially driven ML projects from concept to production.
• Expert Python and solid SQL; modern development practices (Git, tests, code review, CI/CD).
• Hands‑on design and operation of MLOps pipelines: containerisation (Docker/K8s), automated deployment, drift monitoring, retraining triggers.
• Strong grasp of data‑warehousing and modelling concepts; comfortable collaborating with Data Engineering on ETL and architecture.
• Familiarity with Deep‑Learning frameworks (TensorFlow/PyTorch) and vector databases.

ATTRIBUTES

• Passion for upskilling others and shaping Data‑Science culture.
• Excellent communication and collaboration skills to work effectively in cross‑functional teams.
• The ability to build and maintain meaningful relationships.
• A strong belief in doing the right thing.
• Driven by results.
• Ability to recognise and embrace change.
• Intellectual curiosity.
• Able to analyse, interpret and assimilate information.

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