Machine Learning Engineer

In this role, you will collaborate closely with one of our esteemed clients—a globally recognized leader in their industry, distinguished by their commitment to excellence, innovation, and delivering exceptional value. As a trusted IT consulting partner, Dautom is supporting their strategic initiatives by connecting them with exceptional talent to drive business growth and transformation.

About the Role :

Job Title: Machine Learning Engineer (ML Engineer)

Job Type: 1year + extendable long term contract

Remote from India

We are looking for a Data Scientist and ML Engineer who can build machine learning models and run them reliably in production. You will own the full model lifecycle: problem framing, feature engineering, model development, validation, deployment, and monitoring. You will also build the data pipelines that feed your models, so strong data engineering fundamentals are essential.

Key Responsibilities :

• Frame business problems as ML problems, define success metrics, and agree on evaluation criteria with stakeholders
• Develop, tune, and validate models for forecasting, classification, regression, segmentation, anomaly detection, and recommendation
• Engineer robust features from large structured and time-series datasets, and publish them as reusable, governed feature tables
• Deploy models to production as real-time endpoints and scheduled batch inference jobs
• Build end-to-end MLOps workflows: experiment tracking, model registry, versioning, CI/CD, and champion-challenger promotion
• Monitor production models for data drift, prediction drift, and performance decay, and own retraining strategies
• Build and maintain scalable data pipelines using medallion architecture principles, with data quality checks at every layer
• Explain model behavior using interpretability techniques, and communicate results clearly to non-technical audiences
• Apply statistical rigor through hypothesis testing, experiment design, and uncertainty quantification
• Contribute to team standards for ML code quality, reproducibility, documentation, and model governance

Technical Skills :

Machine Learning Development (Core)

• Algorithms: Strong command of supervised and unsupervised learning, gradient boosting, time-series foecasting, clustering, and ensemble methods
• Frameworks: scikit-learn, XGBoost or LightGBM, statsmodels, and PyTorch or TensorFlow
• Model quality: Cross-validation strategies (including time-based splits), hyperparameter optimization (Optuna or similar), and explainability (SHAP)
• Statistics: Solid foundation in probability, inference, experimental design, and causal reasoning

Model Deployment and MLOps (Core)

• Lifecycle: MLflow for experiment tracking, model registry, and model packaging
• Serving: Real-time model serving endpoints and distributed batch inference at scale
• Operations: Model and data monitoring, drift detection, automated retraining, and alerting
• Engineering: Production-quality Python, unit and integration testing, Git, Docker, and CI/CD pipelines

Data Engineering

• Processing: PySpark and advanced SQL on large datasets
• Lakehouse: Delta Lake, Lakeflow Declarative Pipelines (DLT) or equivalent, and orchestration through Databricks Workflows or Azure Data Factory
• Governance: Unity Catalog or equivalent for access control, lineage, and data discovery

Preferred

• Databricks ML stack: Feature Engineering in Unity Catalog, Model Serving, and Lakehouse Monitoring
• Mathematical optimization with OR-Tools, PuLP, or Gurobi
• Marketing analytics: attribution, media mix modeling, customer lifetime value, or churn modeling
• Familiarity with GenAI and LLM integration in ML workflows
• Experience with ERP or CRM source data (for example, SAP)

Qualifications :

• 4 to 10 years of experience in data science or ML engineering, with at least 2 years deploying and maintaining models in production.
• A demonstrable track record of models that delivered measurable business impact, not only offline accuracy gains.
• Bachelors or Masters degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
• Ability to own problems end to end, from raw data to a monitored production model.
• Clear communication of technical findings to business stakeholders.
• Relevant certifications (Databricks Machine Learning Professional, Azure Data Scientist Associate) are a plus

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