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.
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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).
