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DevOps Consultancy

DevOps combines development and operations to increase efficiency, speed, and security.

– DevOps Consultancy

DevOps Consulting Services

Triumph Studio can help businesses from developing a DevOps strategy tailored to the business’s unique needs, to implementing tools and processes that enable continuous integration and deployment, to providing ongoing support and guidance as the business continues to evolve and grow. 

– DOING CLOUD RIGHT

Accelerate DevOps

Increased agility & security

Implementation of advanced technologies that enable rapid response to changing market conditions and threats.

Full stack engineering

Ceveloping both front-end and back-end components, as well as handling the entire software development process from design to deployment.

Automated compliance

Allowing businesses to meet regulatory requirements efficiently and with minimal manual intervention.

Don't learn the hard way

Let the professional take over. We have the experience to help you avoid the pitfalls.

Cloud automation and DevOps

Reduce costs by automating the management and deployment of cloud resources and software applications.

Production ready, no fuss

Thoroughly tested, optimized, and ready for deployment without any hassle or complications.

– Hire best-in-class talent

DevOps Engineer Selection & Hiring Process

Our DevOps engineer selection and hiring process involves identifying the necessary skills, experience, and cultural fit for the role, as well as assessing candidates’ problem-solving and collaboration abilities.

– Step 1

Initial Contact & Discussion

Identifying the client’s specific requirements and evaluating potential candidates based on their technical abilities and cultural fit.

–  Step 2

Answering your questions

Addressing any concerns or queries related to the hiring process, including technical assessments, interviewing, and onboarding procedures.

– Step 3

Candidate Selection

Evaluating potential candidates based on their technical expertise, soft skills, and cultural fit with the organization.

– Step 4

Final Run through

The final run-through includes a review of the top candidates, making the final decision, and extending an offer to the chosen candidate.

– Step 5

Contract Signing & Project Kickoff

After finalizing the candidate, we proceed with contract signing and project kickoff to start the DevOps engineering project.

 

Contact us for more information and a free quotation

Contact Us

Job Description

Job Title: AI Engineer (Systems and Automation)
Location: On-site + Remote (Hybrid Australia)

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 the 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 and 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

  • Delivery of production-grade AI systems that are observable, reliable, and cost-efficient.
  • Implementation of automation opportunities that measurably reduce cycle time or operational load.
  • Contribution to 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
Salary Range: $80K + Super