The Role
Build, deploy, and maintain production machine learning models and scalable ML pipelines in cloud-native environments. Perform EDA, feature engineering, model deployment and monitoring, collaborate with engineering and business stakeholders, and contribute to AI/LLM capabilities using enterprise ML platforms such as AWS SageMaker.
Summary Generated by Built In
AgileEngine is an Inc. 5000 company that creates award-winning software for Fortune 500 brands and trailblazing startups across 17+ industries. We rank among the leaders in areas like application development and AI/ML, and our people-first culture has earned us multiple Best Place to Work awards.
WHY JOIN US
If you're looking for a place to grow, make an impact, and work with people who care, we'd love to meet you!
ABOUT THE ROLE
We are looking for a Senior Data Scientist to develop, deploy, and maintain production machine learning models in a cloud-native enterprise environment. You will build and improve data pipelines supporting ML workflows, perform exploratory data analysis and feature engineering, and collaborate with engineering and business stakeholders to deliver scalable ML solutions using AWS SageMaker or equivalent enterprise ML platforms. The role requires 4+ years of production ML experience with strong Python and advanced SQL skills, and includes contribution to AI and LLM-based capabilities where applicable.
WHAT YOU WILL DO
- Develop, deploy, and maintain production machine learning models.
- Perform exploratory data analysis and feature engineering.
- Build and improve data pipelines that support ML workflows.
- Collaborate with engineering and business stakeholders to deliver scalable ML solutions.
- Contribute to AI/LLM-based capabilities where applicable.
MUST HAVES
- 4+ years of experience building and maintaining production machine learning models.
- Strong Python programming skills.
- Experience with AWS SageMaker or another enterprise ML platform, such as Vertex AI or Azure ML, supporting production ML pipelines.
- Experience deploying and monitoring ML models in production, not only notebook-based development.
- Advanced SQL skills.
- Git and version control experience.
- Experience working independently in production environments.
- Experience building scalable ML solutions in enterprise environments.
- Familiarity with cloud-based ML platforms and production deployment best practices.
- Strong communication skills and the ability to work with cross-functional teams.
- Upper-intermediate English level
NICE TO HAVES
- Experience with MLOps tools such as MLflow, Airflow, dbt, or similar.
- Experience with Snowflake.
- Marketing, growth, experimentation, or causal inference experience.
- Experience with LLMs or AI agents.
- Familiarity with Agile development practices.
PERKS AND BENEFITS
- Growth without limits: build your skills through mentorship, internal TechTalks, challenging projects, and a dedicated annual learning budget
- Competitive compensation: get recognition that reflects your skills and impact, with regular performance and compensation reviews
- Flexibility: work 100% remotely with flexible hours that support focus, autonomy, and a healthy work rhythm
- Meaningful, modern projects: build impactful products using modern technologies alongside global teams and leading brands
- Collaborative culture: join a supportive environment with zero micromanagement where ideas are welcomed and contributions are recognized
- Well-being & support: access local well-being programs and people-focused support tailored to your location
Skills Required
- 4+ years of experience building and maintaining production machine learning models
- Strong Python programming skills
- Experience with AWS SageMaker or Vertex AI or Azure ML (enterprise ML platforms)
- Experience deploying and monitoring ML models in production
- Advanced SQL skills
- Git and version control experience
- Experience working independently in production environments
- Experience building scalable ML solutions in enterprise environments
- Familiarity with cloud-based ML platforms and production deployment best practices
- Strong communication skills and ability to work with cross-functional teams
- Upper-intermediate English level
- Experience with MLOps tools such as MLflow, Airflow, dbt
- Experience with Snowflake
- Marketing, growth, experimentation, or causal inference experience
- Experience with LLMs or AI agents
- Familiarity with Agile development practices
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The Company
What We Do
AgileEngine is a privately held company established in 2010 that builds dedicated teams of designers and developers. We turn good ideas into awesome software that people actually want to use. Some of the biggest names and the hottest startups around the world chose us to build their tech.
