Enterprise AI & Data Architect

Mane Consulting; Canberra; On-site; AUD 180,000 - 260,000; Full time

Job summary

Mane Consulting seeks an AI architecture leader to shape enterprise AI strategy across a national-scale organisation. You will own end-to-end AI architectures, define governance, and drive scalable, compliant platforms that deliver meaningful societal impact.

You will mentor AI/ML and data engineering teams, partner with product and business leaders to identify opportunities, and establish reusable blueprints spanning ingestion, model lifecycles and deployment.

Qualifications

• 10–15 years in AI/ML roles, with deep experience in LLMs/agentic AI, architecture and governance.
• 3–5 years designing enterprise AI solutions using platforms like Gemini/VertexAI, agent frameworks, RAG pipelines.
• 5–7 years designing ingestion frameworks, APIs, semantic search, data normalization and access controls.
• 8+ years in AI/ML engineering with architectural/leadership responsibilities.
• Hands-on with ML/DL frameworks: TensorFlow, PyTorch, Scikit-learn.
• Proven track record deploying production‑grade AI/ML systems at scale.
• Expertise in data engineering and large dataset pipelines.
• Experience with NLP, deep learning and generative AI techniques.
• Strong Python skills and familiarity with the 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

• Own end-to-end AI architecture across multiple domains, data flows and model lifecycles.
• Design reusable AI/ML solution blueprints for ingestion, feature stores, training, deployment.
• Establish standardised MLOps frameworks and reference implementations.
• Partner with product and business leaders to identify AI opportunities and shape options.
• Define governance for model approval, explainability, versioning and drift monitoring.
• Integrate cloud services into cohesive, scalable AI platforms.
• Collaborate with security and compliance to ensure resilience and cost optimisation.
• Provide technical leadership and mentoring across AI teams.
• Create and maintain architectural artefacts, diagrams, standards and docs.

Skills

AI architecture

LLMs & agentic AI

MLOps

Python

Cloud platforms

Data pipelines

Tools

TensorFlow

PyTorch

Scikit-learn

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.