Enterprise AI & Data Architect

Mane Consulting; Canberra; On-site; AUD 180,000 - 280,000; Full time

Job summary

Mane Consulting is seeking a senior AI leader to shape organization-wide AI capability. You will own end-to-end AI architectures, spanning data flows, model lifecycle, and deployment, across Azure and AWS, driving impact beyond product silos.

You’ll establish standardized MLOps, collaborate with product and security teams, define governance, and mentor data science and engineering colleagues. This role offers a chance to deliver long-term societal value while solving complex, meaningful

Qualifications

• 10-15 years in AI/ML-related roles, including deep experience with LLMs and agentic AI (architecture, governance, platform strategy).
• 3-5 years designing enterprise AI solutions using platforms such as Gemini/Vertex AI, agent frameworks, RAG pipelines, grounding/data connectors, and LLM orchestration.
• 5-7 years designing ingestion frameworks, APIs, semantic search, data normalisation, attribute-level access control, PII masking, and connector factory patterns.
• 8+ years in AI/ML engineering with architectural or technical leadership responsibilities.
• Strong hands-on experience with ML/DL frameworks (TensorFlow, PyTorch, Scikit-learn).
• Proven experience deploying production-grade AI/ML systems at scale.
• Expertise in data engineering and pipeline design for large datasets.
• Experience with NLP, deep learning, and generative AI techniques.
• Strong Python skills and familiarity with the broader AI ecosystem.
• Experience across AWS, Azure, or GCP.
• Solid understanding of MLOps, CI/CD, model monitoring, and versioning.
• Excellent problem-solving and architectural design capability.

Responsibilities

• Owning the end-to-end AI architecture across multiple domains, including data flows, model lifecycle, and serving patterns across Azure and AWS.
• Designing reusable AI/ML solution blueprints covering ingestion, feature stores, training pipelines, registries, deployment, monitoring, retraining, and decommissioning.
• Establishing standardised MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) and reference implementations used across engineering and data science teams.
• Partnering with product, data, and business leaders to identify AI opportunities and shape solution options aligned to performance, reliability, cost, and latency requirements.
• Defining governance for model approval, explainability, versioning, drift monitoring, and compliance with privacy and regulatory obligations.
• Integrating cloud-native services (Azure ML, AWS SageMaker, Lambda, EC2, S3, Functions, API gateways, monitoring stacks) into cohesive, scalable AI platforms.
• Working closely with security, compliance, data, and enterprise architecture teams to ensure AI systems meet standards for resilience, observability, and cost optimisation.
• Providing technical leadership and mentoring across AI, ML, and data engineering teams.
• Creating and maintaining architectural artefacts, solution diagrams, standards, and documentation for AI platforms and reference solutions.

Skills

AI architecture

Enterprise AI

MLOps

Cloud platforms

Python

TensorFlow

PyTorch

LLMs

Agentic AI

Data engineering

Tools

MLflow

Kubeflow

Airflow

Docker

Kubernetes

Job description

Are you an AI leader who wants your work to have genuine purpose? not just optimise ad clicks or tweak another SaaS workflow? You'll be shaping AI capability in an organisation where the work has real purpose, long‑term impact, and contributes to outcomes that genuinely matter. It's a chance to build something enduring, architecting AI systems that support a mission far bigger than any single product or platform.
We're partnering with an organisation operating at national scale, where AI is being used to solve complex, meaningful challenges that directly benefit communities and future generations. Define and govern end‑to‑end AI and ML architectures across a diverse portfolio of enterprise use cases, from prediction and personalisation to anomaly detection, automation, and next‑generation agentic systems. This is a rare opportunity to influence AI strategy at an enterprise level while contributing to projects that deliver long‑term societal value.

What you'll be doing;

• Owning the end‑to‑end AI architecture across multiple domains, including data flows, model lifecycle, and serving patterns across Azure and AWS.
• Designing reusable AI/ML solution blueprints covering ingestion, feature stores, training pipelines, registries, deployment, monitoring, retraining, and decommissioning.
• Establishing standardised MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) and reference implementations used across engineering and data science teams.
• Partnering with product, data, and business leaders to identify AI opportunities and shape solution options aligned to performance, reliability, cost, and latency requirements.
• Defining governance for model approval, explainability, versioning, drift monitoring, and compliance with privacy and regulatory obligations.
• Integrating cloud‑native services (Azure ML, AWS SageMaker, Lambda, EC2, S3, Functions, API gateways, monitoring stacks) into cohesive, scalable AI platforms.
• Working closely with security, compliance, data, and enterprise architecture teams to ensure AI systems meet standards for resilience, observability, and cost optimisation.
• Providing technical leadership and mentoring across AI, ML, and data engineering teams.
• Creating and maintaining architectural artefacts, solution diagrams, standards, and documentation for AI platforms and reference solutions.

What you'll bring;

• 10-15 years in AI/ML‑related roles, including deep experience with LLMs and agentic AI (architecture, governance, platform strategy).
• 3-5 years designing enterprise AI solutions using platforms such as Gemini/Vertex AI, agent frameworks, RAG pipelines, grounding/data connectors, and LLM orchestration.
• 5-7 years designing ingestion frameworks, APIs, semantic search, data normalisation, attribute‑level access control, PII masking, and connector factory patterns.
• 8+ years in AI/ML engineering with architectural or technical leadership responsibilities.
• Strong hands‑on experience with ML/DL frameworks (TensorFlow, PyTorch, Scikit‑learn).
• Proven experience deploying production‑grade AI/ML systems at scale.
• Expertise in data engineering and pipeline design for large datasets.
• Experience with NLP, deep learning, and generative AI techniques.
• Strong Python skills and familiarity with the broader AI ecosystem.
• Experience across AWS, Azure, or GCP.
• Solid understanding of MLOps, CI/CD, model monitoring, and versioning.
• Excellent problem‑solving and architectural design capability.