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Machine Learning & AI Engineer | SQL at DyFlex Solutions

Roles & Responsibilities

  • Junior to intermediate hands-on experience in data engineering, analytics engineering, or machine learning-adjacent roles in production environments
  • Strong proficiency in SQL and Python, with the ability to write efficient queries, build pipelines, and support analytical and ML workflows
  • Experience working with modern data or processing frameworks such as Apache Spark, Airflow, dbt, Kafka, or similar tools
  • Practical exposure to machine learning pipelines, applied ML use cases, MLOps concepts, or agent-based / automated workflows is highly regarded

Requirements:

  • Build and maintain scalable data and machine learning pipelines for ingesting, transforming, and delivering data into production environments
  • Develop and maintain SQL-driven data models, reports, and analytical outputs that support real business use cases
  • Design, build, and deploy cloud-based solutions across AWS, Azure, or GCP
  • Apply modern engineering best practices, including version control, CI/CD, and infrastructure as code

Job description

Machine Learning & AI Engineer

DyFlex is an SAP Platinum Partner delivering high-quality SAP solutions across Australia. We’re now expanding our Data and AI practice to match the strength and reputation of our established SAP capability.

As a Machine Learning & AI Engineer, you’ll design, build, and deploy scalable data and machine learning solutions that deliver real business value. You’ll use strong SQL and modern cloud platforms to create reliable pipelines and support ML workloads. Experience with tools like Spark or Databricks is a plus but not required. You will work on meaningful technical challenges with autonomy and communicate technical outcomes clearly to both technical and non-technical stakeholders.

We value engineers who think creatively, communicate effectively, and engage confidently with stakeholders. We’re looking for engineers who do more than write code. You’ll listen to client challenges, dig into the core problem, help shape solutions, and explain them clearly. If you want to build something from the ground up with a team that’s already proven it can deliver meaningful outcomes, we’d like to hear from you.

Your tasks and responsibilities:

  • Build and maintain scalable data and machine learning pipelines for ingesting, transforming, and delivering data into production environments
  • Develop and maintain SQL-driven data models, reports, and analytical outputs that support real business use cases
  • Manage and optimise databases, data warehouses, and cloud storage solutions, including platforms such as Databricks, Snowflake, or cloud-native services
  • Implement data quality checks, validation processes, and testing to ensure reliable, production-ready systems
  • Design, build, and deploy cloud-based solutions across AWS, Azure, or GCP
  • Contribute to practical machine learning solutions, including feature engineering, model integration, and pipeline automation
  • Take ownership of clearly defined technical components, working independently while collaborating closely with senior engineers and stakeholders
  • Engage with internal teams and clients to understand business problems and translate them into workable technical solutions
  • Apply modern engineering best practices, including version control, CI/CD, and infrastructure as code

Your qualifications and experience:

  • Junior to intermediate hands-on experience in data engineering, analytics engineering, or machine learning-adjacent roles in production environments
  • Strong proficiency in SQL and Python, with the ability to write efficient queries, build pipelines, and support analytical and ML workflows
  • Experience working with modern data or processing frameworks such as Apache Spark, Airflow, dbt, Kafka, or similar tools
  • Practical exposure to machine learning pipelines, applied ML use cases, MLOps concepts, or agent-based / automated workflows is highly regarded
  • Solid understanding of relational databases, data modelling, and query optimisation
  • Experience working with cloud data platforms such as Databricks, Snowflake, or comparable technologies
  • Degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field, or equivalent practical experience
  • Strong problem-solving mindset with the ability to work independently, adapt quickly, and approach problems creatively
  • Excellent communication skills, with the ability to clearly explain technical concepts to both technical and non-technical stakeholders

What we offer:

  • Work with SAP’s latest technologies on cloud as S/4HANA, BTP and Joule, plus Databricks, ML/AI tools and cloud platforms
  • A flexible and supportive work environment including work from home
  • Competitive remuneration and benefits including novated lease, birthday leave, remote working, additional purchased leave, and company-provided laptop
  • Competitive remuneration and benefits including novated lease, birthday leave, salary packaging, wellbeing programme, additional purchased leave, and company-provided laptop Comprehensive training budget and paid certifications (Databricks, SAP, cloud platforms)
  • Structured career advancement pathways with mentoring from senior engineers
  • Exposure to diverse industries and client environments

Join a renowned organisation delivering projects to some of Australia’s leading enterprises

DyFlex is committed to providing a safe, flexible and respectful environment for staff free from all forms of discrimination, bullying and harassment. We are proud of our diverse and inclusive team as only together we can continually improve ourselves and achieve best outcomes for our customers– we are the region's leading SAP Platinum Partner!

Please note that we cannot offer visa sponsorship for this role. Applicants must have full working rights in Australia.

This role is open to Sydney-based applicants only and operates in a hybrid work environment.

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