Software Engineer

dentsu Austria

Machine 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.

Posted 7/7/2026full-timeMumbai • 🇮🇳 IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading AI and ML initiatives, including designing and maintaining ML pipelines, model evaluation, and establishing MLOps best practices. Proficient in collaborating with cross-functional teams to ensure compliance with security policies and responsible AI principles.

Highest-signal resume keywords

Machine Learning Model DeploymentPython ProgrammingMLOps ToolingModel Evaluation TechniquesAgile Methodologies

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningDeep LearningModel EvaluationFeature EngineeringData IngestionA/B TestingDrift DetectionSQLDockerAPIs

Tools & Technologies

PyTorchTensorFlowScikit-learnHugging FaceAirflowKubeflowMLflowWeights & BiasesVertex AISageMaker

Industry Keywords

MLOpsCI/CDAgileSecurity PoliciesResponsible AILarge-Scale Data ToolingModel RegistryVersion Control

Tech Stack

Tools & technologies

AirflowCloudDockerGoogle 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.