Overview

Operations Associate Jobs in Gauteng, South Africa at Dariel

Title: Operations Associate

Company: Dariel

Location: Gauteng, South Africa

Strategic Operational AI Associate (AI Delivery & Enablement)

Reporting to: Executive of Automation & AI Engineering

Works closely with: Executive of Automation Engineering, Product & Project Managers, Delivery Leads, Engineering Leads, RPA and Software Engineering teams

Role Summary

The Strategic Operational AI Associate acts as the senior strategic delivery partner to the Executive of Automation & AI Engineering. This role bridges AI strategy and operational execution, ensuring AI and automation initiatives move from early-stage concepts into production-grade, governed, and measurable delivery.

The role combines strong AI, cloud, and engineering credibility with a delivery focus. While not a purely hands-on engineering position, it requires deep technical fluency across AI, ML, MLOps, and AWS, with selective hands-on involvement to unblock teams, guide architectural decisions, and accelerate delivery when needed.

Approximately 60% of the role is operational and delivery-focused, ensuring execution discipline, transparency, and accountability. The remaining 40% is strategic, shaping use cases, influencing prioritisation, and enabling the engineering organisation to adopt modern AI and cloud delivery practices.

A key part of the role is enablement: supporting RPA, Java, and traditional engineering teams as they transition into AI‑driven and cloud‑native ways of working through coaching, reference implementations, and repeatable engineering patterns.

Key Responsibilities

1. AI Use Case Shaping & Technical Evaluation

  • Support the early identification and shaping of AI and automation use cases, helping teams define clear scope, feasibility, dependencies, and success criteria.
  • Apply a strong technical lens to ensure use cases are realistically buildable, testable, and operationalisable.
  • Partner with Product Managers and Delivery Leads on prioritisation, sequencing, and delivery readiness.
  • Translate high-level ideas into clearly defined backlogs with explicit trade-offs and acceptance criteria.

2. Delivery Enablement & Execution Oversight

  • Introduce delivery clarity, transparency, and accountability across AI and automation initiatives.
  • Support teams in converting backlogs into working, production-grade solutions aligned to engineering best practices.
  • Track delivery health using meaningful engineering and delivery metrics (e.g. DORA metrics).
  • Step in selectively with hands-on technical guidance to unblock critical delivery risks.

3. AI, Cloud & Engineering Enablement

  • Coach and support engineering teams (including RPA- and Java-heavy teams) in adopting modern AI, ML, and cloud-native delivery approaches.
  • Create and promote repeatable patterns, reference architectures, and working examples for AI and MLOps delivery.
  • Encourage strong CI/CD, DevOps, and automation practices across AI and software delivery pipelines.

4. Cloud, MLOps & Governance Alignment

  • Provide guidance across AWS-based AI platforms, ensuring solutions align with enterprise cloud standards.
  • Support the establishment of strong MLOps practices, including model evaluation, monitoring, and lifecycle management.
  • Ensure appropriate model governance, security, moderation, and compliance practices are embedded into delivery from day one.

5. Stakeholder Partnership & Influence

  • Act as a trusted technical and delivery advisor to senior leaders across automation, AI, engineering, and product.
  • Translate complex technical concepts into clear, actionable guidance for non-technical stakeholders.
  • Balance speed, quality, risk, and governance in a pragmatic, outcome-focused way.

Experience & Qualifications

Essential

  • BSc in Computer Science, Engineering, or a related technical discipline.
  • Strong experience in Machine Learning Engineering, MLOps, and/or Data Science.
  • Solid software engineering fundamentals with experience working alongside engineering teams.
  • Deep understanding of DevOps, CI/CD pipelines, and modern delivery practices.
  • Extensive AWS cloud experience, ideally with hands-on exposure and certifications.
  • Experience with AWS Bedrock, SageMaker, and Agent Core is highly advantageous.
  • Familiarity with ML frameworks and model evaluation techniques.
  • Strong understanding of information security principles and best practices.
  • Experience with Agile/Scrum delivery environments.
  • Working knowledge of model moderation, evaluation, and governance.
  • Proven ability to operate at both strategic and execution levels.

Desirable

  • Architecture experience (formal or informal).
  • Exposure to enterprise-scale automation and AI programs.
  • Project or delivery management experience in complex engineering environments.

What Success Looks Like

  • AI and automation use cases move from idea to production with clearer scope, stronger governance, and faster delivery.
  • Engineering teams are more confident and capable in delivering AI and cloud-native solutions.
  • Delivery risks are surfaced early, managed proactively, and communicated transparently.
  • Senior leaders gain clearer visibility into progress, trade-offs, and outcomes across AI initiatives.
  • The organisation develops repeatable, scalable patterns for AI delivery rather than one-off solutions.

Apply

Upload your CV/resume or any other relevant file. Max. file size: 800 MB.