Machine Learning Engineer

Sydney (Hybrid Working)

FinTech

Role Overview

We are seeking a highly skilled Machine Learning Engineer with deep expertise in AWS machine learning services and strong data science fundamentals. You will be responsible for the full lifecycle of machine learning initiatives from data ingestion and preparation, through model development and training, to deployment and ongoing monitoring in production.

This role will see you working closely with technology, product, and operations teams to identify opportunities where machine learning can drive measurable business impact, design robust solutions, and ensure seamless integration into core platforms.

Key Responsibilities

Data Exploration & Preparation

• Gather, clean, and analyse datasets from multiple internal and external sources.
• Develop and maintain scalable data pipelines using AWS services such as Glue, Athena, and Redshift.

Model Development & Training

• Build, train, and optimise ML models in AWS SageMaker for use cases such as anomaly detection, predictive modelling, and data quality improvement.
• Perform feature engineering, selection, and rigorous evaluation of models.

Deployment & MLOps

• Deploy models to production using SageMaker endpoints or other AWS deployment mechanisms.
• Implement monitoring, alerting, and retraining workflows to ensure continued model performance.
• Manage model versioning, governance, and documentation.

Collaboration & Communication

• Partner with business and product teams to define ML use cases and success metrics.
• Translate complex technical outputs into clear, actionable business insights.
• Present findings and recommendations effectively to both technical and non-technical stakeholders.

Required Skills & Experience

• Proven experience with the AWS ML stack: SageMaker, Glue, Athena, Redshift, Kinesis, Lambda, S3.
• Strong data science background: statistics, ML algorithms, feature engineering, and model evaluation.
• Solid data engineering expertise: ETL design, data lake/lakehouse patterns, streaming data ingestion.
• Proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow) and SQL.
• Experience with MLOps practices: CI/CD pipelines for ML, model monitoring, and retraining strategies.
• Understanding of data governance, security, and compliance requirements (experience in financial services is a plus).
• Excellent problem-solving skills, with the ability to work independently in a fast-paced environment.

Desirable

• Experience working with financial, capital markets, or operational datasets.
• Knowledge of AWS Bedrock or other generative AI tools.
• Exposure to time-series forecasting, anomaly detection, or fraud detection.
• Familiarity with Infrastructure-as-Code (Terraform, CloudFormation).
• Experience with emerging AI frameworks and agentic AI systems, such as:
• RAG (Retrieval-Augmented Generation)
• MCP (Model Context Protocol)
• LangGraph
• CrewAI
• Other agent-based orchestration frameworks for AI systems

To learn more or apply, get in touch at [email protected]