Enterprise AI & Data Architect

Mane; Canberra; On-site; AUD 260,000 - 360,000; Full time

Job summary

Mane Consulting specialise in Data & Analytics. We seek a senior AI architect to define and govern end-to-end AI and ML architectures across Azure and AWS, guiding ML ops, governance, and scalable platforms. You’ll mentor teams, design reusable blueprints, and partner with executives to align AI initiatives with impact and cost goals.

The role requires extensive hands-on leadership, deep knowledge of LLMs, and a track record deploying production AI at scale in a complex enterprise.

Qualifications

• 10–15 years in AI/ML roles, including deep experience with LLMs and agentic AI.
• 3–5 years designing enterprise AI solutions using platforms such as Gemini/Vertex AI and LLM orchestration.

Responsibilities

• Own the end-to-end AI architecture across multiple domains, including data flows, model lifecycle, and serving patterns across Azure and AWS.
• Design reusable AI/ML solution blueprints covering ingestion, feature stores, training pipelines, registries, deployment, monitoring, retraining, and decommissioning.
• Establish standardised MLOps frameworks (MLflow, Kubeflow, Airflow, Docker, Kubernetes) and reference implementations used across engineering and data science teams.
• Partner 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

LLMs & agentic AI

Python

NLP & Generative AI

Data engineering

MLOps

Model governance

Problem solving

Enterprise AI strategy

Tools

TensorFlow

PyTorch

Scikit-learn

MLflow

Kubeflow

Airflow

Docker

Kubernetes

Azure ML

SageMaker

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.

Mane Consulting specialise in Data & Analytics.