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Drive data governance and quality improvements for an energy-sector company, shaping digital strategy and cloud-based data capabilities while bridging technical and business teams.
Builds and optimizes scalable data pipelines using Python, Spark, and SQL to transform raw business requirements into efficient, cloud-based (AWS) data solutions, ensuring data quality, automation, and collaboration with cross-functional teams.
Build and deploy AI/ML models (traditional, generative, and agentic) using Python, AWS, and Snowflake to improve insurance workflows and client experiences.
Lead AI/ML initiatives including traditional models, GenAI, and agentic systems to improve customer and employee experiences using Python, cloud platforms, and modern data tools.
Lead advanced analytics and ML initiatives for an insurer, partnering with business leaders to design and deploy scalable models that drive strategy in marketing, underwriting, and fraud prevention.
Secure AI applications and agentic systems on Google Cloud Vertex AI by embedding security controls, guardrails, and secure-by-default practices across the AI lifecycle.
Designs and builds cloud data platforms on AWS, Azure, and GCP using SQL, Python, and ETL best practices for enterprise clients.
Designs and builds real-time data pipelines on AWS using PySpark, Airflow, and Redshift to power enterprise-scale analytics and insights for an HR-focused SaaS platform.
Build and maintain AWS-based data pipelines and analytics platforms using services like Glue, Redshift, and SageMaker to enable data-driven insights and reporting.
Data Engineer | Sydney, NSW We are looking for an experienced Data Engineer (9–14 Years) to join a high-performing team delivering enterprise-scale data platforms and real-time data solutions on AWS. Must Have Skills…
Build and operate an LLM-powered content generation pipeline with safety guardrails, RAG grounding, and image generation, deployed on AWS serverless services.
Build and maintain scalable AWS-based data pipelines and lakehouse architecture, using Python and PySpark to ingest, transform, and serve scholarly publishing data for analytics and AI initiatives.
Build, train, and deploy ML models in Python using TensorFlow/PyTorch to solve business problems and maintain production systems.
Design and deploy AI/ML pipelines and GenAI apps to personalize trading experiences, predict customer churn, and automate marketing workflows using Python, SQL, and AWS SageMaker.
Build and own the cloud infrastructure and MLOps platform for a regulated fintech firm’s AI trading systems on AWS, including Kubernetes, CI/CD, and model deployment pipelines.
Design and implement cloud infrastructure, CI/CD pipelines, and GenAI solutions using Azure, AWS, Python, and Kubernetes.
Design and deploy scalable AI/ML systems, including generative AI solutions with RAG and embeddings, to solve complex business problems and drive measurable impact.
Builds and deploys AI/ML models from prototype to production, focusing on MLOps pipelines, model monitoring, and scalable infrastructure for public-sector and energy clients using PyTorch, TensorFlow, and cloud platforms like AWS SageMaker.
Lead the design, automation, and operations of StarHub’s cloud data platform using AWS, Snowflake, and SageMaker to support scalable AI and analytics workloads.
Build secure, cloud-native apps and AI-enabled platforms for government digital and cybersecurity initiatives using Python, Terraform, and CI/CD pipelines.
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