Senior AI Engineer

Job Purpose

The Senior AI Engineer designs, builds, deploys, and maintains advanced AI and machine learning systems that enable secure, scalable, and production-grade AI solutions across the Hollard Group. This role acts as a senior technical authority, contributing to enterprise AI engineering, platform enablement, MLOps, agentic AI, and AI security.

Key Responsibilities

AI Solution Engineering

• Design, implement, and optimise end-to-end AI/ML solutions (data prep, training, evaluation, deployment, monitoring).
• Build reusable components such as pipelines, APIs, micro-models, and AI modules.
• Lead development of scalable ML workflows using MLOps toolchains.
• Ensure secure, observable, and robust deployment patterns aligned to enterprise standards.

AI Agents & Agentic Systems

• Design, build, deploy, and maintain AI agents using Microsoft AI Agent Services, Azure OpenAI, and enterprise-aligned tooling.
• Engineer advanced agentic capabilities including:
• Planning and autonomous task execution
• Short-term and long-term memory architectures
• Tool invocation and extensible toolchains
  • Mentor and guide mid-level and junior engineers.

Lead technical design discussions and establish engineering best practices.

• Retrieval-Augmented Generation (RAG) and hybrid retrieval patterns
• Event-driven and reactive execution flows
• Build, maintain, and extend MCP (Model Context Protocol) servers and MCP tools.
• Orchestrate multi-agent and multi-tool interactions for complex enterprise workflows.
• Ensure all agents are production-grade, secure, observable, and compliant with Hollard’s governance and AI risk frameworks.

Architecture, Platform & Policy Alignment

• Ensure strict adherence to Hollard’s IT Development, Security, and Architecture Policies.
• Contribute to evolution of the enterprise AI platform and reference architectures.
• Implement standards for observability, testing, versioning, governance, and secure deployment.
• Ensure alignment with enterprise cloud, data, and integration architectures.

API Exposure & Integration Layer

• Expose AI capabilities and agent functions through RESTful APIs.
• Design versioned API interfaces using OpenAPI/Swagger.
• Integrate with enterprise systems via APIs, events, microservices, and messaging patterns.
  • Event streams and event-driven patterns
  • Messaging architectures (queues, topics, service buses)
  • Microservices and integration-layer patterns
  • Work closely with architects to ensure alignment with enterprise integration standards.

User Interaction & UI Enablement

• Build or contribute to lightweight front-end applications enabling interaction with AI agents.
• Develop chat-based, form-based, or task-oriented UI experiences using approved web technologies.
• Work with design teams to ensure intuitive, accessible user experiences for AI-driven capabilities.

Responsible, Secure & Compliant AI

• Apply Responsible AI principles throughout the ML lifecycle.
• Conduct robustness, fairness, explainability, and model security assessments.
• Ensure compliance with POPIA, FSCA, PA Standards, DAMA, and AI governance requirements.

Value Delivery & ROI Focus

• Translate business needs into scalable, value-driven AI engineering solutions.
• Identify opportunities to improve performance, reliability, and cost-
• Support measurement of business impact delivered by AI systems.

Technical Leadership & Mentoring

• Represent AI Engineering across squads and cross-functional initiatives.

Required Experience (relevant or in a similar role)

• 6–8+ years in software engineering, ML engineering, or AI system development.
• Proven delivery of AI/ML systems into production.
• Strong exposure to cloud-based AI tooling, MLOps frameworks, and AI security.

Required Knowledge and Skills

Core Engineering Skills

• Strong proficiency in C# and .NET, complementing Python-based AI/ML development.
• Deep understanding of agent architecture patterns, including:
• Tool invocation frameworks
• Prompt engineering and orchestration
• RAG design and vector-based memory retrieval
• Multi-agent coordination and workflow orchestration
• Memory persistence, vector stores, and contextual state management

AI/ML & MLOps Competencies

• Strong proficiency with Microsoft Azure (Azure ML, Azure AI Services, Azure OpenAI, Azure Storage, Azure DevOps).
• Proficiency in Python, SQL, and ML/AI frameworks (PyTorch, TensorFlow, scikit-learn).
• Strong knowledge of MLOps (CI/CD, feature stores, orchestration, drift monitoring).
• Experience with APIs, microservices, containerisation, and cloud-native engineering.
• Advanced AI security capabilities: secure model handling, threat modelling, monitoring.

Leadership Competencies

• Technical excellence and architectural thinking
• Cross-team collaboration and influence
• Mentorship & capability building
• Engineering discipline, quality focus, delivery accountability
• Ethical judgement and responsible decision-making