Machine Learning Engineer

OverviewMachine Learning Engineer position at Salient Group, Sydney (Hybrid Working) in FinTech. Role involves end-to-end machine learning initiatives from data ingestion and preparation to model development, deployment, and monitoring in production. You will work with technology, product, and operations teams to design robust ML solutions and integrate them into core platforms.ResponsibilitiesData Exploration & Preparation: Gather, clean, and analyse datasets from multiple internal and external sources; develop and maintain scalable data pipelines using AWS services (Glue, Athena, Redshift); 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; 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 to both technical and non-technical stakeholders.Required Skills & ExperienceProven 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.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, ability to work independently in a fast-paced environment.Experience 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 (e.g., RAG, LangGraph, CrewAI, or other agent-based orchestration frameworks).Additional InformationLocation: Sydney, NSW (Hybrid)Application contact: [email protected] #J-18808-Ljbffr

Created: 2025-10-04