Conduct code reviews and mentor team members on engineering standards
Proactively identify technical risks and communicate them early to course-correct
Participate in roadmap planning, scoping, and technology feasibility assessments
Contribute to a culture where solving customer problems is always the highest priority
Required
B.S. in Computer Science, Mathematics, Statistics, or a related quantitative field; M.S. or Ph.D. preferred
5+ years of software engineering experience, with 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 ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost
Experience building and deploying production ML systems — model training, evaluation, versioning, and serving
Proficiency with AWS data and AI services: SageMaker, S3, Glue, Athena, Lambda, EC2, CloudWatch
Experience with data pipeline tooling: Apache Spark, Airflow, dbt, or equivalent
Solid understanding of data modeling, SQL, and working with large-scale databases (PostgreSQL, MSSQL, or similar)
Strong grasp of software engineering fundamentals: CI/CD, DevOps, testing, and system design
Familiarity with REST API design, microservices, and containerization (Docker, Kubernetes)
Experience with Agile development methodologies
Nice to Have
Experience with LLMs, prompt engineering, or RAG (Retrieval-Augmented Generation) systems
Familiarity with MLflow, Weights & Biases, or other ML lifecycle management tools
AWS Certification (Machine Learning Specialty, Solutions Architect, or equivalent)
Experience with geospatial data, catastrophe modeling, or climate/weather datasets
Full-stack experience with Angular or React and .NET Core
Background in the insurance, reinsurance, or financial services industries
