About Us

As a global player in the SaaS automotive landscape, Titan Dealer Management Solutions (Titan DMS) boasts over 145 staff globally and SaaS representation across more than 26 markets. Titan is now recognised as one of the leading suppliers of technologically driven industry solutions to automotive dealers and manufacturers around the world.

Since 2009, Titan’s flagship product, the Titan Dealer Management System (Titan DMS), has been adopted by Franchised Automotive Dealerships covering over 45 different Automotive Brands throughout Australia, Asia, Pacific Region, Middle East, UK, Africa and Europe, and continues to extend its global reach and reputation as a leading international software provider.

About the Role

The AI Engineer is responsible for designing, developing, and integrating AI-powered capabilities into the Titan DMS platform.

Working closely with Product, Engineering, UX, and AI team members, this role combines software engineering with practical AI product development. You'll be responsible for taking AI concepts from prototype through to production, ensuring solutions are reliable, secure, scalable, and deliver genuine business value. Solutions will need to integrate with Titan's existing C#/.NET and WinForms applications while supporting the company's AI strategy.

AI Solution Development

• Design, develop, and maintain AI-powered features and software products.
• Build applications using modern Large Language Models (Anthropic, Google Gemini, OpenAI and similar).
• Integrate LLMs into existing C#/.NET and WinForms applications.
• Build AI Agents, prompt engineering strategies, and workflow automation solutions.
• Design and implement RAG and knowledge retrieval solutions.
• Ensure AI solutions are scalable, secure, and production ready.

Software Engineering

• Participate in solution design and technical discussions.
• Write clean, maintainable, and well-tested code.
• Develop REST APIs for AI service integrations.
• Develop modern web applications using React, Angular or similar frameworks.
• Perform code reviews and follow engineering standards.
• Troubleshoot production issues and optimise AI performance.
• Continuously improve AI reliability, latency, and cost efficiency.

AI Governance & Security

• Ensure AI solutions comply with Titan’s cybersecurity, privacy and responsible AI standards. 
• Protect customer and company data when integrating with external AI platforms.
• Monitor AI quality, hallucination risk, reliability and security.
• Support AI testing, evaluation and continuous optimisation.
• Evaluate different LLM providers and models to optimise quality, speed and cost.

Collaboration & Continuous Improvement

• Work closely with Product, Engineering, UX, and QA teams.
• Participate in sprint planning and Agile ceremonies.
• Research emerging AI technologies and recommend improvements.
• Share knowledge and contribute to AI engineering best practices.

Skills, Qualifications & Experience

Required Experience

• Minimum 4 years' software engineering experience.
• Strong experience with C#/.NET development.
• Experience in developing modern web applications using React, Angular or similar frameworks.
• Experience developing or integrating LLM / Generative AI solutions into enterprise or SaaS applications.
• Experience integrating AI services through REST APIs.
• Experience with Prompt Engineering, AI Agents, and workflow automation.
• Experience designing or implementing Retrieval-Augmented Generation (RAG) and knowledge retrieval solutions.
• Experience working with AWS cloud services.
• Strong knowledge of SQL Server and relational databases.
• Experience with Git, Docker, and CI/CD practices.
• Good written and verbal English communication skills.

Qualifications

• Bachelor's degree in computer science, Software Engineering, Artificial Intelligence, or related discipline.

Preferred

• Experience with Amazon Bedrock, OpenAI API, Anthropic Claude, or other LLM platforms.
• Experience with LangChain, LangGraph, or similar AI orchestration frameworks.
• Experience with vector databases (pgvector, Pinecone, Weaviate, Milvus).
• Experience working within a SaaS product environment.
• Experience in working with Voice or SMS enabled AI software products.

Key Success Measures

• Successful delivery of AI-powered product features.
• Reliable, secure, and scalable AI integrations.
• High-quality software delivered on schedule.
• Continuous improvement in AI solution performance and reliability.
• Strong collaboration with Product and Engineering teams.
• Adoption of AI engineering best practices.