Applied AI Engineer (Data-Focused)

About the Role

We are looking for an Applied AI Engineerwith strong data engineering fundamentals to help build and deploy real-world AI solutions. This is not a research position you will focus on practical implementation, data pipelines, and AI-powered product development.

You will work closely with software and business teams to design systems that extract, process, and transform data into usable intelligence.

Key Responsibilities

- Build and deploy applied AI/ML solutions for business use cases

- Design and maintain data pipelines for structured and unstructured data

- Perform data extraction, cleaning, transformation, and feature preparation

- Work with databases and data storage systems (SQL and NoSQL)

- Integrate AI models into production workflows and APIs

- Collaborate with full-stack and backend engineers on AI-enabled products

- Use modern AI tools (LLMs, copilots, automation) to improve productivity and delivery

Required Skills & Qualifications

- 15 years of experience in applied AI, ML engineering, or data-driven software development

- Strong knowledge of Python and AI/ML libraries (e.g., scikit-learn, PyTorch, TensorFlow)

- Solid experience with databases (PostgreSQL, MySQL, MongoDB, etc.)

- Experience building ETL/ELT pipelines and working with data workflows

- Familiarity with cloud platforms (AWS, Azure, or GCP) is a plus

- Comfortable deploying models into real systems (not just notebooks)

Must-Have Mindset

- AI tool usage is expected you actively leverage modern AI assistants and automation tools

- Highly proactive, self-directed, and able to work autonomously

- Strong ownership mentality with a can-do attitude

- Practical problem solver focused on shipping real outcomes

Benefits & Perks

- Competitive Salary Up to AUD 1800 (Gross)

- Hybrid work model: work from home **12 days per week**

- First month is fully in-office (100%) for onboarding and team integration

- Company-sponsored AI development tools: Cursor, Claude Code, ChatGPT

- High-autonomy environment with opportunities to own real product outcomes

- Collaborative team culture focused on execution and learning

Nice to Have

- Experience with vector databases and retrieval systems (RAG pipelines)

- Knowledge of MLOps tools (MLflow, Docker, CI/CD)

- Exposure to real-time data processing (Kafka, streaming systems)