AI Engineering Team Lead for Large-Scale Banking Solutions

6 days ago

Sydney, New South Wales, Australia Commonwealth Bank of Australia Full time

We are seeking an experienced AI Engineer to lead our team in developing innovative solutions for Australia's largest bank and fintech. As a Staff Software AI Engineer, you will be responsible for designing, developing, and deploying advanced AI and machine learning models that align with business objectives.

With a strong track record of developing AI models and deploying them in production environments, you will collaborate with cross-functional teams to deliver scalable AI solutions. You will also coach other team members and influence senior stakeholders on modern best practices.

This role involves conducting experiments to evaluate model performance, refining models based on findings, and collaborating with Data engineers to deploy AI models in production environments. You will also lead research initiatives to explore new AI models and methodologies that can be applied to current and future products.

As an ideal candidate, you have proven hands-on experience in automated LLM outcome validation, metrication of AI outputs, and expertise in Python, MLOps, Vector DB, Dynamo DB, Langfuse, MLflow, Glue, Bleu, traditional machine learning, and DevSecOps practices. You also have demonstrated experience in big data platform technologies such as Spark framework, Iceberg file formats, Redshift, and relational databases.

Estimated salary: $150,000 - $200,000 per year. We offer a hybrid work arrangement with at least half your time each month connecting in office. We also provide flexible working options, including changing start and finish times, part-time arrangements, and job share.

Benefits:

  • Flexible working arrangements
  • Hybrid work setup
  • Professional development opportunities
  • Collaborative and inclusive work environment

Requirements:

  • 5+ years of experience in AI engineering and software development
  • Proven track record of delivering AI solutions in production environments
  • Strong commercial experience implementing solutions using RAG, Prompt Engineering, LLM models
  • Expertise in Python, MLOps, Vector DB, Dynamo DB, Langfuse, MLflow, Glue, Bleu, traditional machine learning, and DevSecOps practices

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