Soni Resources Group

We are seeking a full-time Analytics and ML Ops Engineer focused on developing and maintaining infrastructure to support data pipelines, analytics workflows, and machine learning processes. The position involves cross-functional collaboration and aims to improve how data and models are deployed and scaled to support organizational decision-making. It’s ideal for someone who enjoys problem-solving, working across teams, and enhancing systems that drive data insights.

Position Responsibilities:

• Design, implement, and maintain scalable pipelines that support data ingestion, transformation, and accessibility across teams.

• Collaborate with data scientists and analysts to deploy models, streamline experimentation, and maintain reproducibility and versioning.

• Optimize data storage and query performance, with a focus on cost efficiency and scalability in cloud environments.

• Apply best practices in ML Ops, including model versioning, automated deployment workflows, and metadata tracking.

• Identify opportunities to manage infrastructure as code and improve system automation using tools like Terraform.

• Automate analytics workflows and stages of the model lifecycle to improve efficiency and deployment speed.

• Build monitoring and alerting tools to ensure the reliability of data pipelines and dashboards.

• Modernize legacy systems and contribute to cloud-native transitions using containerized or serverless approaches.

• Partner with engineering and DevOps to enhance version control workflows and automate infrastructure through CI/CD pipelines.

• Support continuous integration and delivery for both model development and analytics systems.

• Define and uphold data quality standards and governance protocols.

• Apply best practices in access control, security, and compliance to protect sensitive information and ensure platform integrity.

• Provide hands-on support for issue resolution, testing, and code review to optimize data and ML operations across teams.

• Act as a bridge between analytics, ML, and engineering groups to support strategic data initiatives.

Minimum Qualifications:

• Bachelor’s degree in Computer Science, Data Science, or a related technical field.

• 5+ years of experience in data engineering, analytics infrastructure, or ML operations.

• Proven track record with ML pipeline development and CI/CD practices in collaborative settings.

• Experience maintaining production-level data and ML systems.

• Proficiency in Python and SQL.

• Familiarity with scripting (e.g., Bash), Linux systems, and cloud environments.

• Experience with containerization tools (e.g., Docker, Kubernetes) and job orchestration frameworks.

• Strong skills in version control (e.g., Git) and DevOps practices.

• Exposure to infrastructure-as-code tools (e.g., Terraform).