ModelOps Automation Developer

Aptonet; United States; À distance; USD 120 000 - 160 000; Plein temps

Résumé du poste

Aptonet is seeking a ModelOps Automation Developer to design and build Python-based solutions in Databricks that enable AI model lifecycle management. You will create reusable libraries, automate environments connected to Snowflake, and implement robust CI/CD pipelines for testing, deployment, and retraining.

You will collaborate with data scientists, engineers, and business teams to deliver scalable, reliable model deployment, monitoring, and governance.

Qualifications

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Statistics, or a related field.
• Five or more years of data engineering development experience, including software development and automation.
• Advanced Python programming skills.
• Hands-on experience building solutions within Databricks, including reusable libraries, integrations, or automated workflows.
• Strong experience with database platforms such as Snowflake, Oracle, or Microsoft SQL Server.
• Basic familiarity with Linux/UNIX command-line environments.
• Strong analytical and problem-solving skills.
• Strong written and verbal communication skills, with the ability to explain technical concepts to business and technical audiences.
• Ability to work independently, collaborate across teams, and manage multiple priorities in a deadline-driven environment.

Responsabilités

• Design and build Python-based solutions within Databricks to support AI enablement and model operations.
• Develop reusable libraries and components for authentication, authorization, logging, and reporting.
• Automate creation and configuration of model development environments with Snowflake connectivity.
• Create CI/CD pipelines to support model testing, deployment, versioning, monitoring, and retraining.
• Develop APIs and integrations connecting data pipelines, databases, and enterprise systems.
• Collaborate with data science, actuarial, engineering, and citizen developer teams to translate business needs into technical solutions.
• Support data preparation, cleansing, and schema design for ML initiatives.
• Apply ModelOps best practices for governance, access controls, and regulatory compliance.
• Troubleshoot workflow and integration issues and optimize performance.
• Develop familiarity with insurance modeling applications, including mortality, morbidity, and fraud.

Connaissances

Python

Databricks

CI/CD pipelines

Snowflake

SQL Server / Oracle

Linux/UNIX

Data engineering

Automation

Formation

Bachelor’s degree in Computer Science or related

Master’s degree (preferred)

Outils

Jenkins

Terraform

AWS CloudFormation

Snowflake

Databricks

Description du poste

We are seeking a ModelOps Automation Developer to design and build software solutions that support AI enablement and automate the machine learning model lifecycle. This role focuses on Python development within Databricks, creating reusable capabilities and workflows that help data scientists develop, deploy, and maintain models efficiently.

Key projects include developing shared libraries for authentication, authorization, logging, and reporting, and automating the creation of model development environments with connectivity to Snowflake schemas. The developer will collaborate with data scientists and engineering teams to build scalable, reliable solutions that support model deployment, monitoring, versioning, and governance.

Key Responsibilities

• Develop and maintain Python-based solutions within Databricks to support AI enablement and model operations.
• Build reusable libraries and components for common capabilities, including authentication, authorization, logging, and reporting.
• Automate the creation and configuration of model development environments, including connectivity to Snowflake schemas.
• Develop automated workflows and CI/CD pipelines to support model testing, deployment, versioning, monitoring, and retraining.
• Implement ingestion and scoring pipelines, and adapt Python, Scala, and SQL code for API and web-based access.
• Develop APIs and integrations connecting data pipelines, databases, and enterprise systems.
• Collaborate with data science, actuarial, engineering, and citizen developer teams to translate business requirements into technical solutions.
• Support data preparation, cleansing, schema design, and analysis for machine learning initiatives.
• Apply ModelOps best practices for reliability, access controls, documentation, governance, and regulatory compliance.
• Troubleshoot workflow and integration issues and optimize the performance of automated solutions.
• Develop familiarity with insurance modeling applications, including mortality, morbidity, and fraud.

Required Qualifications

• Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Statistics, or a related field.
• Five or more years of data engineering development experience, including software development and automation.
• Advanced Python programming skills.
• Hands-on experience building solutions within Databricks, including reusable libraries, integrations, or automated workflows.
• Strong experience with database platforms such as Snowflake, Oracle, or Microsoft SQL Server.
• Basic familiarity with Linux/UNIX command-line environments.
• Strong analytical and problem-solving skills.
• Strong written and verbal communication skills, with the ability to explain technical concepts to business and technical audiences.
• Ability to work independently, collaborate across teams, and manage multiple priorities in a deadline-driven environment.

Preferred Qualifications

• Master’s degree in Computer Science, Data Engineering, Information Systems, Statistics, or a related field.
• Experience integrating Databricks solutions with Snowflake.
• Experience developing and maintaining Jenkins CI/CD pipelines.
• Experience with DevOps practices and Infrastructure as Code tools, including Terraform or AWS CloudFormation.
• Knowledge of AWS services such as EMR, Step Functions, Lambda, EC2, S3, RDS, DynamoDB, and Bedrock.
• Experience with ModelOps or MLOps practices, including model deployment, versioning, monitoring, governance, and automated retraining.