Software Engineer

Job Description:

Machine Learning Engineer

Role Purpose

The Machine Learning Engineer will lead the AI and ML roadmap for the Digital Innovation & Product Hub, designing and shipping production-grade machine learning systems that power the Media teams at dentsu. From building robust training and inference pipelines, to defining how we evaluate model quality and business impact, this role owns the full ML lifecycle, partnering with Product Managers, specialism leads, data engineers and developers to turn business problems into deployed, monitored, well-evaluated models that drive measurable outcomes for our clients.

Accountabilities

Core Accountabilities

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

Skills

We’re keen to meet anyone who’s comfortable with most of the below and are flexible if you have strengths or weaknesses in particular areas.

Professional

Good communication skills and ability to communicate complex ideas

Ability to comprehend business challenges and then articulate potential solutions

Strong attention to detail and highly organised

Ability to self-manage, working as part of the wider team

Believe in clean coding and simple solutions

Outcome-focused – comfortable framing ML work in terms of business impact and able to prioritise a roadmap against competing demands

Technical

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

Location:

DGS India - Mumbai - Thane Ashar IT Park

Brand:

Merkle

Time Type:

Full time

Contract Type:

Permanent