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Tech Stack
Tools & technologiesAirflowAngularApacheAWSCloudDockerEC2KubernetesMicroservicesPostgresPythonPyTorchReactScikit-LearnSparkSQLTensorflow.NET
About the role
Key responsibilities & impact- Design, build, and deploy machine learning models and AI-powered features into production SaaS products
- Maintain scalable data pipelines for ingestion, transformation, and enrichment of large, complex datasets
- Develop model-serving infrastructure using AWS SageMaker, Lambda, and container-based deployment patterns
- Apply LLM integrations, RAG architectures, and generative AI capabilities to enhance product functionality
- Own data quality, observability, and monitoring for AI/ML workloads in production
- Lead the design and implementation of cloud-native microservices and APIs using Python and C#/.NET on AWS
- Drive best practices in design, code quality, and system design
- Contribute to requirements review, design, development, testing, and deployment
- Conduct code reviews and mentor team members
- Identify technical risks and communicate them early
- Participate in roadmap planning, scoping, and technology feasibility assessments
- Prioritize solving customer problems
Requirements
What you’ll need- B.S. in Computer Science, Mathematics, Statistics, or a related quantitative field required; M.S. or Ph.D. preferred
- 5+ years of software engineering experience, including at least 2 years in a senior or lead role on cloud-native AWS products
- Strong Python skills for data engineering, ML pipelines, and API development
- Hands-on experience with scikit-learn, PyTorch, TensorFlow, or XGBoost
- Experience building and deploying production ML systems, including model training, evaluation, versioning, and serving
- Proficiency with AWS SageMaker, S3, Glue, Athena, Lambda, EC2, and CloudWatch
- Experience with Apache Spark, Airflow, dbt, or equivalent
- Understanding of data modeling, SQL, and large-scale databases such as PostgreSQL or MSSQL
- Knowledge of CI/CD, DevOps, testing, and system design
- Familiarity with REST API design, microservices, Docker, and Kubernetes
- Experience with Agile development methodologies
- Nice to have: LLMs, prompt engineering, RAG systems, MLflow, Weights & Biases, AWS Certification, geospatial data, catastrophe modeling, climate/weather datasets, Angular or React, .NET Core, and insurance, reinsurance, or financial services experience
Benefits
Comp & perks- Health Insurance
- Retirement Plan
- Disability benefits
- Paid Time Off program
- Work flexibility
- Support, coaching, and training
- Short Term Incentive