1. Purpose of the Position
The Lead Data & AI Architect is responsible for designing, engineering and overseeing advanced data, software and AI systems supporting Symsafe’s automation and managed services platforms. The role provides strategic and technical leadership across cloud engineering, AI, MLOps, data governance, distributed systems and software architecture.
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2. Key Responsibilities
Software Engineering & Architecture
• Architect, design and implement enterprise-grade software systems supporting Symcontrol automation.
• Lead development of scalable APIs, cloud-native services and system integrations.
• Establish engineering best practices, patterns, code reviews and quality standards.
AI & Machine Learning
• Design AI models and inference pipelines to support predictive automation and system intelligence.
• Implement MLOps frameworks for training, validation, deployment and monitoring of AI systems.
• Conduct feasibility studies and architecture assessments for AI/ML solutions.
Big Data & Cloud Platforms
• Architect distributed data pipelines, real-time processing and analytics ecosystems.
• Design secure, scalable cloud architectures across AWS/Azure/GCP environments.
• Oversee data lake/data warehouse design, governance and optimisation.
Cybersecurity & Compliance
• Ensure all AI and software systems meet Australian cybersecurity standards.
• Implement secure coding practices, access controls and data protection frameworks.
Technical Leadership
• Provide technical direction to engineering teams and contractors.
• Mentor junior developers and data engineers.
• Contribute to enterprise technology strategy and roadmap.
Stakeholder Engagement
• Work with clients, internal teams and partners to define technical requirements.
• Prepare architecture documents, technical specifications and executive briefings.
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3. Skills & Experience
Essential:
• 8+ years in software engineering, data architecture or AI engineering roles.
• Deep experience with cloud-native platforms (AWS/Azure/GCP).
• Expertise in Python, Java, distributed systems, microservices and containerisation.
• Strong capability in AI/ML model development and MLOps.
• Experience designing large-scale data platforms (Spark, Kafka, Databricks, Snowflake etc.).
Desirable:
• Cybersecurity knowledge (identity, access, audit).
• Experience in automation, RMM platforms, observability and monitoring systems.
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4. Qualifications
Bachelor’s or Master’s degree in Computer Science, Software Engineering, AI, Data Engineering or related field.
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5. Performance Indicators
• Successful delivery of scalable AI/data architectures
• Quality, reliability and security of engineered systems
• Technical leadership outcomes
• Contribution to technology roadmap and business growth
