FREE ACCESS
5,000–10,000 jobs/day
See all jobs on JobTailor
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Software Engineer
dentsu AustriaMachine Learning Engineer driving AI and ML projects for dentsu's Media teams. Collaborating with cross-functional teams to build and deploy production-grade ML systems.
Tech Stack
Tools & technologiesAirflowCloudDockerGoogle Cloud PlatformPythonPyTorchScikit-LearnSQLTensorflow
About the role
Key responsibilities & impact- Leading the AI and ML roadmap for the team, identifying high-value opportunities, prioritising against business impact, and translating strategic goals into a clear, sequenced plan of ML initiatives.
- Designing, building and maintaining end-to-end ML pipelines covering data ingestion, feature engineering, training, validation, deployment and retraining - with reproducibility, scalability and observability baked in.
- Owning model evaluation - defining offline and online metrics, building eval sets, running A/B tests and validating models for accuracy, fairness, robustness and business impact before and after deployment.
- Establishing MLOps best practices across the team - experiment tracking, model registry, versioning, CI/CD for models, and infrastructure-as-code - alongside clear technical documentation.
- Monitoring models in production, detecting drift, debugging performance regressions, and iterating to keep latency, cost and accuracy within agreed thresholds.
- Partnering with developers, data engineers and Product Managers to expose models via well-designed APIs, and working with specialism leads to embed ML capabilities into Media team workflows.
- Collaborating with our internal Security and Legal teams to ensure models comply with dentsu’s Security Policies, data handling standards and responsible-AI principles.
Requirements
What you’ll need- Strong experience training, fine-tuning and deploying machine learning models in production, with a solid grounding in classical ML and modern deep learning.
- Strong Python skills, with hands-on experience using ML frameworks such as PyTorch, TensorFlow, scikit-learn and Hugging Face, plus working with SQL and large-scale data tooling.
- Experience building ML pipelines and MLOps tooling - e.g. Airflow, Kubeflow, MLflow, Weights & Biases, Vertex AI or SageMaker - and deploying models on cloud (GCP ideally).
- Well versed in agile methodologies, Git and version control best practices.
- Deep experience with model evaluation - offline metrics, eval set design, A/B testing, drift detection, fairness checks and validating models against business KPIs.
- Comfortable with Docker, containerised model serving and exposing models via APIs for downstream developers and applications.
- Exposure to LLMs, RAG or generative AI is a bonus, but not essential.
Benefits
Comp & perks- Sustainability is a vital part of our business and an important area of focus for our clients.
- We create opportunities for connection and collaboration between our colleagues and clients, building a sense of belonging and having some fun along the way.
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
Machine LearningDeep LearningModel EvaluationFeature EngineeringData IngestionA/B TestingDrift DetectionSQLDockerAPIs