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The job in short
Building machine learning models.
PythonDjangoFlaskSQLAirflowdbtKubernetesDockerMachine learningMarketingInsurance
Details
- Found
- 21 d ago · posted 2026-10-04
- Location as listed
- Atlanta, Georgia, United States
- Role
- Machine Learning / AI Engineer
- Source
- The company’s own careers site
Full description
We are supporting a client in the Risk and Insurance industry in the search for a driven Machine Learning Engineer to help us bring software development rigor to the ML life-cycle within the business insurance industry. As an experienced innovator partnering with the marketing and growth teams, this is a massive opportunity to drive high growth impact at a hyper-growth startup. Your job is to be a full-stack ML engineer, supercharging all aspects of scaling Machine Learning at NEXT: application design and architecture, scalable deployment of inference solutions as APIs, data enrichment as a service, and model monitoring.
You will be joining an innovative division of based in Atlanta: Data Labs. The mandate of Data Labs is to build software and data solutions that meaningfully impact marketing, funnel, risk, and servicing/claims experiences.
What You’ll Do:
• Empower our team of data scientists to rapidly develop and deploy ML solutions.
• Leverage software engineering best practices to create and deploy data-intensive and machine learning inference products.
• Understand the data and dig deep to extract actionable insights.
• Think creatively and outside the box to answer desired experimental questions as well as expose opportunities to create business value.
• Work cross-functionally with marketing, engineering, product, senior management, and external partners.
What We Need:
• 3+ years of hands-on experience in the complete software development life-cycle with demonstrated professional experience with machine learning
• Strong command of Python and the standard web development frameworks (e.g. Django, Flask, FastAPI) & fluency with database technologies, SQL, and Python data packages
• Demonstrated experience deploying models and applications to a cloud environment using tools like Docker and Kubernetes.
• Demonstrated experience with software engineering best practices, such as Test-Driven Development and continuous integration/deployment pipeline
• Experience with ML-oriented data pipelining tools (dbt, Airflow, Prefect, DVC, etc.)
Unstoppable Qualities:
• Experience in the insurance industry (strong fintech/lending experience will also be considered)
• Experience with GitLab
Relocation assistance: Limited assistance
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