Senior Model Developer (MLOps & Databricks)
Full Time - 6 PM - 3 AM IST ShiftNoida
About the role
- We are looking for experienced Senior Model Developers with strong hands-on expertise in MLOps, Databricks, Git, and containerized deployment environments.
- The role involves supporting the development, migration, implementation, operationalization, and ongoing management of statistical and machine learning models, with a focus on transitioning model development and operational processes to Databricks.
- The ideal candidate will combine strong technical implementation capabilities with a sound understanding of statistical modeling methodologies, model performance, and outputs, enabling effective collaboration with model developers, data engineers, and business stakeholders.
- Two positions are available. Complementary technical strengths across selected candidates may be considered, with hands-on MLOps and Databricks experience remaining a key priority.
Job Description
- Support the migration, implementation, and operationalization of statistical and machine learning models within Databricks environments.
- Develop, maintain, and manage model workflows using Python, SQL, and Databricks.
- Use Git for version control, code management, collaboration, and traceability of model development activities.
- Work with Docker and containerized environments to enable consistent and reproducible model execution and deployment.
- Implement and maintain model lifecycle workflows, including execution, testing, monitoring, deployment, and production support.
- Collaborate with model developers and business stakeholders to translate modeling requirements into scalable technical solutions.
- Understand model methodologies, assumptions, inputs, outputs, and performance metrics to ensure accurate implementation and execution.
- Perform model testing, troubleshooting, performance monitoring, and production issue resolution.
- Maintain technical documentation covering model implementation, version control, operational workflows, and deployment processes.
- Contribute to standardizing model development, deployment, and operational practices across teams.
Qualifications
- Relevant educational qualifications in Computer Science, Information Technology, Data Science, Statistics, Mathematics, Engineering, or a related discipline.
- Hands-on experience in model development, implementation, migration, operationalization, or production support.
- Strong practical experience working with Databricks for model development, execution, workflow management, or operationalization.
- Practical experience with Git repositories, branching, merging, pull requests, and collaborative version control.
- Hands-on experience creating, managing, or deploying applications and model workloads using Docker and containerized environments.
- Strong programming experience in Python for data processing, model implementation, automation, and troubleshooting.
- Strong SQL skills for querying, transforming, and validating data.
- Experience with MLOps practices, including model lifecycle management, deployment workflows, reproducibility, monitoring, and operational support.
- Good understanding of statistical modeling methodologies, machine learning techniques, model evaluation metrics, and interpretation of model outputs.
- Exposure to model migration from legacy environments to cloud-based platforms will be an advantage.
- Experience supporting marketing analytics, customer propensity, response, or risk models will be preferred.
- Exposure to model governance, development documentation, and regulatory expectations in the banking industry will be an added advantage.
- Databricks: Model development, execution, workflow management, model operationalization, and deployment.
- MLOps: Model lifecycle management, operational workflows, deployment, reproducibility, testing, monitoring, and production support.
- Version Control: Git repositories, branching, merging, pull requests, code management, and collaborative development.
- Containerization: Docker, container creation and management, and deployment of model workloads in containerized environments.
- Programming & Data Management: Python, SQL, data processing, data transformation, validation, automation, and troubleshooting.
- Modeling Fundamentals: Statistical modeling, Machine Learning, regression, classification, tree-based algorithms, predictive modeling, model evaluation metrics, and interpretation of model outputs.
- Preferred Tools & Technologies: MLflow, Databricks Jobs/Workflows, Model Registry, PySpark, distributed data processing, CI/CD pipelines, and automated model testing and deployment.
- Domain Exposure: Marketing Analytics, Customer Propensity, Customer Response, Risk Modeling, and Banking.
- Model Governance: Model documentation, versioning, governance practices, and regulatory expectations in banking.
- Collaboration: Ability to work effectively with statistical model developers, data engineers, technology teams, and business stakeholders to resolve technical and operational challenges.
- Key Competencies: MLOps, Databricks, Git, Docker, Python, SQL, Model Operationalization, Model Migration, Machine Learning, Statistical Modeling, Model Deployment, and Production Support.
Perks & Benefits
Medical Insurance
Choose from Health Savings Plan, HRA, or PPO, whichever best fits your healthcare needs and budget.
Parental leave
Take the time you need to welcome and care for your growing family, with leave designed to support you through this milestone.
Training and development
Access structured, project-specific training and continuous learning opportunities that help you grow in your role and career.
Two-way transport
Enjoy stress-free commuting with two-way cab pick-up and drop-off facilities for every shift.
