AI & ML Solutions Engineer

Jones Grove IT Recruiting

Charlotte Bay

On-site

AUD 130,000 - 140,000

Full time

14 days+

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Benefits offered by this job

Medical insurance

Vision insurance

401(k)

Job summary

A leading IT recruiting agency in Australia is seeking an experienced AI & ML Solutions Engineer to lead the design, development, and deployment of AI/ML solutions. You will collaborate with internal stakeholders to deliver impactful business outcomes through innovative technology. The ideal candidate has 3–5+ years of experience in data science and is proficient in Python and various AI technologies. This role offers a competitive salary ranging from $130,000 to $140,000 annually.

Qualifications

3–5+ years of experience in data science or ML engineering.
Strong Python skills and familiarity with cloud-native workflows.
Ability to work with stakeholders and deliver outcomes.

Responsibilities

Build and maintain ML and GenAI solutions using various tools.
Run proof-of-concepts to validate ideas and scale into production.
Translate business challenges into technical solutions.

Skills

Python

Machine Learning Engineering

Data Science

End-to-End ML Pipeline Delivery

GenAI Tools and Frameworks

Tools

Databricks

Snowflake

Power BI

Job description

Jones Grove IT Recruiting provided pay range

This range is provided by Jones Grove IT Recruiting. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.

Base pay range

$130,000.00/yr - $140,000.00/yr

Direct message the job poster from Jones Grove IT Recruiting

We are looking for a hands-on, consultative technologist to lead the design, development, and deployment of AI/ML and GenAI solutions across the business. This is a high-impact role for someone who thrives at the intersection of engineering, data science, and business outcomes.

As the AI & ML Solutions Engineer, you’ll be the team’s “Swiss Army knife” capable of building end-to-end solutions: from shaping business use cases to architecting pipelines, deploying models, and prototyping GenAI applications. You’ll work directly with internal stakeholders to deliver measurable value, helping us automate, predict, and optimize across operations and supply chain.

We’re not looking for a domain expert in one area, we’re looking for a strong generalist who can go deep where needed. If you’re driven to experiment, move fast, and make things real, we want to talk.

What You’ll Do

Own the AI lifecycle – Build, deploy, and maintain ML and GenAI solutions using Databricks, Azure, MLflow, and Snowflake.
Experiment & deliver quickly – Run rapid proof-of-concepts to validate ideas, then scale what works into production.
Automate for impact – Develop Python/R-based workflows, ML pipelines, and API endpoints to automate business processes.
Lead solution design – Translate ambiguous business challenges into technical solutions, including MLOps infrastructure, model selection, and GenAI frameworks.
Collaborate cross-functionally – Work closely with data engineers, architects, and business stakeholders to align solutions with strategic priorities.
Enhance team capability – Share best practices and contribute to a culture of experimentation, continuous learning, and delivery.

The Tech Stack

Visualization/Apps: Power BI, Power Automate, Teams-integrated tools

What We’re Looking For

Must-Haves:

3–5+ years of experience in data science, machine learning engineering, or full-stack AI solutioning
Experience with end-to-end ML pipeline delivery from data prep and feature engineering to deployment and monitoring
Strong Python skills and familiarity with Databricks, Snowflake, and cloud-native workflows
Exposure to GenAI tools and frameworks, including building or experimenting with LLM-based applications including OpenAI (GPT-4), Anthropic (Claude), Meta (LLaMA 2/3), Mistral, Google Gemini, and Cohere
Understanding of MLOps practices: model promotion, versioning, retraining, and feature stores
Ability to work with stakeholders, frame business problems, and deliver outcomes—not just models
Comfortable failing fast and iterating quickly based on feedback

Nice to Haves:

Consulting background or experience working directly with non-technical stakeholders
Experience in manufacturing, supply chain, or industrial domains
Familiarity with infrastructure-as-code and Git-based workflows
Experience integrating AI/ML outputs into user-facing tools or internal workflows (e.g., Teams bots, dashboards)

Seniority level