Job Summary
We are seeking an ML Engineer to build, deploy, and operationalize machine learning models as part of a data platform modernization program for an Australian payments company. This role focuses on end-to-end ML pipeline development from feature engineering and model training to deployment, monitoring, and inference using AWS SageMaker and related services. The ideal candidate can translate Data Scientist prototypes into production-grade ML systems with robust monitoring and automation.
Key Responsibilities
Develop end-to-end ML pipelines covering feature engineering, model training, evaluation, deployment, and monitoring on AWS SageMaker.
Build and maintain feature pipelines that feed a centralized feature store for use across multiple ML models (e.g., churn, propensity, segmentation).
Deploy ML models as real-time (endpoint) and batch (scheduled) inference services with SageMaker.
Implement model monitoring, performance tracking, and data/model drift detection mechanisms.
Collaborate with Data Scientists to productionize experimental models and optimize for latency, cost, and reliability.
Build ML pipeline orchestration using SageMaker Pipelines, Step Functions, or equivalent workflow tools.
Integrate model outputs with downstream systems (e.g., Snowflake, Salesforce, BI tools) via reverse ETL or API patterns.
Implement and manage model versioning, A/B testing, and champion/challenger deployment patterns.
Contribute to the MLOps framework, including CI/CD for ML, automated retraining triggers, and experiment tracking (e.g., MLflow).
Required Skills & Experience
Deep hands-on experience with SageMaker for training, hosting, pipelines, and model registry.
Expert-level Python for ML development (scikit-learn,



XGBoost, LightGBM, PyTorch, or TensorFlow).
Hands‐on experience deploying ML models to production endpoints (real-time and batch).
Experience implementing drift detection, performance dashboards, and automated alerting for deployed models.
Understanding of MLOps principles: CI/CD for ML, experiment tracking, model versioning, and automated retraining.
Strong SQL skills for feature extraction and data validation from Snowflake or similar warehouses.
Preferred Skills (Nice‐to‐Have)
Experience with MLflow, Weights & Biases, or equivalent experiment tracking tools.
Familiarity with Snowflake Cortex or Snowpark ML.
Knowledge of LLM integration patterns and AWS Bedrock.
Experience building propensity, churn, or recommendation models in fintech or payments.
Familiarity with Docker, Kubernetes, or containerized ML deployments.
AWS Certified Machine Learning – Specialty certification.
Benefits
Joining Cognizant will give you the prospect to learn and collaborate with some of the most talented people in the industry, while having your finger on the pulse of emerging industry trends and working on the cutting edge of technology in your field of expertise.
We recognize that our people perform at their best when they feel valued as significant contributors and that is why at Cognizant, taking care of our employees is a priority:
You can pursue innovative career tracks and opportunities here.
You can enhance your professional development through education and dedicated training.
We'll give you the skills you need to keep pace with the changing workplace while our compensation, benefits and wellness packages help you stay healthy and plan for the future.
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📌 Ml Engineer (New South Wales)
🏢 Cognizant
📍 New South Wales