About the Role We are looking for an AI Engineer (Systems and Automation) who can build, deploy, and maintain robust AI systems while also designing automation solutions that streamline operations, product workflows, and decision-making. This role blends hands-on engineering, scalable system design, and practical automation using modern AI and MLOps practices.

Key Responsibilities AI Systems Engineering Design, develop, and deploy end-to-end AI solutions-from prototype to production. Build scalable, reliable ML pipelines for data ingestion, training, evaluation, and inference. Implement monitoring for model performance, drift, latency, and reliability. Optimize models and infrastructure for cost, speed, and accuracy. Ensure security, privacy, and compliance across AI systems.

AI Automation Engineering Identify business and technical processes that can be automated using AI. Build AI-powered automation workflows using APIs, agents, orchestration tools, and event-driven architectures. Develop integrations with internal tools (CRM, ERP, HRIS, support platforms, analytics stacks). Create reusable components and templates to accelerate automation delivery. Measure and report automation impact using clear KPIs.

Cross-Functional Delivery Collaborate with Product, Data, Engineering, and Operations to scope and deliver AI initiatives. Translate business needs into technical solutions with pragmatic trade-offs. Document architectures, workflows, and operational runbooks.

Required Qualifications 

• 2-3+ years of experience in AI/ML engineering, automation engineering, or adjacent software roles. 
• Strong programming skills in Python (required); familiarity with TypeScript/Node.js is a plus. 
• Experience with model development using frameworks such as PyTorch, TensorFlow, or similar. 
• Solid understanding of ML lifecycle, including feature engineering, training, evaluation, deployment, and monitoring. 
• Hands-on experience with MLOps practices and tools (CI/CD for ML, model registries, experiment tracking). 
• Experience integrating LLMs into real products or workflows (prompting, RAG, fine-tuning awareness, guardrails). 
• Strong system design skills and comfort working in cloud environments (AWS, GCP, or Azure). 
• Ability to troubleshoot production issues across data, model, and infrastructure layers.

Education 

MS or BS in Artificial Intelligence, Machine Learning, Computer Science, Software Engineering, or a similar program.

Core Technical Skills 

• Data pipelines and orchestration (Airflow, Prefect, Dagster or equivalents). 
• Serving and deployment (Docker, Kubernetes, serverless, REST/gRPC). 
• Observability and monitoring (logs, metrics, tracing; model drift/performance dashboards). 
• Datastores for AI workloads (SQL/NoSQL, vector databases). 
• API design and integration patterns. 
• Familiarity with responsible AI practices, evaluation frameworks, and safety controls.

Good to Have 

• AI research experience demonstrated through: Publications, preprints, or thesis work. 
• Applied research projects in industry labs. 
• Contributions to open-source AI libraries.
• Experience with multi-agent systems, tool-using LLMs, or advanced RAG architectures. Experience in process automation platforms or iPaaS tools. Exposure to regulated or high-compliance environments.

What Success Looks Like 

You deliver production-grade AI systems that are observable, reliable, and cost-efficient. You identify and implement automation opportunities that measurably reduce cycle time or operational load. You help establish best practices for AI governance, deployment, and lifecycle management.

What We Offer 

Competitive compensation and benefits. A high-ownership environment with real-world AI impact. Opportunities to shape AI architecture, automation strategy, and engineering standards.

Equal Opportunity We are an equal opportunity employer and value diversity in backgrounds, experiences, and perspectives. Special Note: Agencies not applicable

Location: On-site+Remote (Hybrid Australia with working rights)

Salary Range: $75-80K