Responsibilities
- Build and test machine learning models to support their platform.
- Design, build, and deploy data and ML pipelines on AWS.
- Enable an iterative lifecycle for data products to improve, integrate, and deploy.
- Standardize workflows, analysis, and modeling for deployment and observability in production.
- Develop monitoring and observability systems for ML models and experiments.
- Collaborate across teams to align modeling with engineering standards.
Basic qualifications
- Education: Bachelor’s or Master’s degree in a quantitative field.
- Experience:
- 2–4 years of relevant experience.
- 4+ years’ experience with Python and ML frameworks.
- 1+ year of experience with MLOps and maintaining ML models at scale.
- Technical Skills:
- Strong knowledge and hands-on experience with:
- Python programming
- SQL and relational databases; ETL processes
- Cloud technologies (AWS, GCP, or Azure)
- Git or other version control systems
- Model versioning/tracking (DVC, MLFlow)
- ML pipeline development/deployment (Metaflow, Kubeflow, Prefect, Dagster)
- Containers (Docker, Kubernetes)
- Visualization and monitoring tools (Dash, Streamlit)
- Modeling/tuning/optimization with frameworks (sklearn, PyTorch)
Preferred qualifications
- Real-time inference deployment and monitoring (FastAPI, Ray Serve).
- CI/CD practices.
- Model deployment strategies (A/B testing, canary release).
- Cross-functional collaboration (DevOps, Data Engineering, Data Science).
- Time series analysis and predictive modeling.
Benefits
- Salary range: $120-150K
- Health and Wellness: Industry-best benefits.
- Work-Life Balance: HYBRID – 2 days in office, 3 days from home.
Tags & focus areas
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