Position Summary

We are seeking an experienced Databricks Data Science Solution Architect to provide in-region technical leadership and hands-on development support to an established APAC Data Science team.

This role is focused specifically on the design, development, deployment, and operationalization of data science and machine learning solutions within Databricks . The successful candidate will have strong Python development experience and a demonstrated background building production data science solutions using Databricks capabilities such as Apps, Jobs, Pipelines, model serving Endpoints, MLflow, and related platform services .

This is not primarily a data engineering role . While data preparation and data management are components of the position, the primary responsibility is to guide data scientists on how to architect, build, deploy, and support production-ready data science applications and machine learning models on Databricks.

The individual will work closely with the APAC Data Science team, ModelOps platform teams, data engineers, and business stakeholders to establish effective development patterns and ensure data science solutions are scalable, maintainable, secure, and production-ready.

Principal Duties and Responsibilities

1. Data Science Solution Design and Architecture

- Provide architectural leadership for data science, machine learning, AI, and advanced analytics solutions developed within Databricks.
- Guide data scientists in translating analytical concepts and models into scalable, production-ready Databricks solutions.
- Design solution architectures incorporating Databricks Apps, Jobs, Pipelines, model serving Endpoints, MLflow, Unity Catalog, and related platform capabilities .
- Establish reusable design patterns and development standards for deploying Python-based data science solutions.
- Review existing data science solutions and recommend improvements to architecture, scalability, maintainability, and operational supportability.

2. Python Data Science Development

- Develop and review production-quality Python code supporting machine learning, predictive analytics, statistical modeling, and data science applications.
- Work directly with data scientists to convert notebooks, prototypes, and analytical models into maintainable production applications.
- Establish Python development standards, project structures, reusable libraries, testing practices, dependency management, and deployment patterns.
- Troubleshoot Python-based data science applications and provide technical guidance on performance, reliability, and maintainability.

3. Databricks Application and Workload Development Design, develop, and support data science solutions using Databricks capabilities including:

- Databricks Apps
- Databricks Jobs and Workflows
- Databricks Pipelines
- Model Serving Endpoints
- MLflow
- Unity Catalog
- Databricks Asset Bundles
- Notebooks and Python development environments
- Databricks compute and cluster environments

Guide the team in selecting the appropriate Databricks components based on application requirements and deployment objectives. 4. Machine Learning Model Deployment and ModelOps

- Guide data scientists through the complete model lifecycle from experimentation and development through production deployment and ongoing support.
- Establish best practices for:
- Model packaging
- Model versioning




- Experiment tracking
- Model registration
- Automated testing
- Deployment
- Model serving
- Monitoring
- Retraining
- Use MLflow and Databricks capabilities to promote reproducible and maintainable machine learning development.
- Support the deployment of Python-based models as Databricks applications, scheduled jobs, pipelines, APIs, and serving endpoints.

5. Technical Leadership and Mentorship

- Serve as a senior technical advisor to the APAC Data Science team.
- Conduct solution design reviews and provide hands-on guidance throughout development.
- Mentor data scientists in software engineering and production development practices.
- Help data scientists transition experimental models and notebooks into production-ready solutions.
- Establish reference implementations, coding standards, reusable patterns, and technical documentation.
- Lead technical workshops and knowledge-sharing sessions on Databricks data science development.

6. Solution Integration

- Design integrations between Databricks data science solutions and enterprise systems, applications, APIs, databases, and downstream consumers.
- Support development of APIs and service-based interfaces for exposing model outputs and analytical functionality.
- Collaborate with data engineering teams where data pipelines or transformations are required to support data science solutions.
- Ensure appropriate use of enterprise authentication, authorization, data access, and security patterns.

7. Performance, Scalability, and Production Support

- Optimize Python and Databricks workloads for performance, reliability, scalability, and cost.
- Troubleshoot production issues involving Databricks applications, jobs, pipelines, models, and endpoints.
- Establish monitoring, logging, observability, and error-handling practices for production data science solutions.
- Recommend appropriate Databricks compute configurations based on data science workloads and application requirements.

8. Collaboration

Partner closely with

- APAC Data Science teams
- ModelOps and AI Enablement teams
- Platform Engineering
- Data Engineering
- Application Development teams
- Business stakeholders
- Information Security and Data Governance teams

Translate analytical and business requirements into practical technical designs and production implementations.

Job Specifications

Education and Experience

Required

- Bachelor's degree in Computer Science, Software Engineering, Data Science, Statistics, Mathematics, Engineering, or a related technical discipline.
- 8+ years of experience in software development, data science, machine learning engineering, solution architecture, or related technical roles.
- 3+ years of hands-on experience designing and developing data science or machine learning solutions within Databricks .
- Solid hands-on Python development experience supporting production data science or machine learning solutions.




- Demonstrated experience moving data science models from experimentation or notebooks into production environments.
- Experience working with cloud platforms such as AWS, Azure, or GCP.

Preferred

- Master's degree in Computer Science, Data Science, Statistics, Machine Learning, Software Engineering, or related discipline.
- Databricks certification.
- Experience supporting enterprise Data Science or Machine Learning teams.
- Experience within insurance, financial services, or another regulated industry.

Required Skills and Abilities

- Advanced Python development skills with experience developing production-quality data science and machine learning solutions.
- Hands-on experience building and deploying data science solutions within Databricks .
- Experience with Databricks capabilities such as:
- Apps
- Jobs / Workflows
- Pipelines
- Model Serving Endpoints
- MLflow
- Unity Catalog
- Databricks Asset Bundles
- Strong understanding of machine learning and data science development workflows.
- Experience deploying predictive models and analytical applications into production.
- Experience converting notebooks and experimental models into maintainable production solutions.
- Understanding of MLOps / ModelOps principles including:
- Experiment tracking
- Model registration
- Version control
- Automated testing
- CI/CD
- Deployment
- Monitoring
- Model retraining
- Experience with Git and modern software development lifecycle practices.
- Experience developing or integrating APIs and application services.
- Ability to troubleshoot Python, Databricks, and machine learning application issues.
- Strong understanding of software engineering principles including modular development, testing, documentation, and source control.
- Ability to mentor and guide experienced data scientists on production development practices.
- Excellent communication skills and the ability to explain technical architecture and development concepts to both technical and non-technical stakeholders.

Preferred Skills

- Experience with Posit Workbench and Posit Connect .
- Experience with R in addition to Python.
- Experience with Spark and PySpark.
- Experience with REST APIs and microservice architectures.
- Experience with CI/CD tools such as Azure DevOps, Jenkins, GitHub Actions, or similar platforms.
- Experience with model monitoring and observability.
- Knowledge of Generative AI or LLM-based application development within Databricks.
- Experience developing interactive analytical or data science applications.
- Knowledge of enterprise security, governance, and data access controls.
- Experience with insurance, actuarial, risk, or financial-services data science applications.

Key Candidate Profile The strongest candidate will be someone who has actually built data science and machine learning solutions in Databricks using Python , rather than someone whose Databricks experience is primarily focused on data engineering. We are specifically seeking candidates who can demonstrate experience taking a Python-based data science model or application and implementing it as a production Databricks solution using capabilities such as Databricks Apps, Jobs, Pipelines, MLflow, and model serving Endpoints .

The individual must also be comfortable acting as a technical leader and mentor to an existing team of data scientists, helping them determine how their solutions should be designed and developed within Databricks .

📌 Databricks Data Science Solution Architect (India)
🏢 Aptonet
📍 India