Senior Software Engineer

Responsibilities

• 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 where appropriate 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 (Python, C#/.NET) on AWS
• Drive best practices in design, code quality, and system design across the team
• Contribute to all stages of the SDLC: requirements review, design, development, testing, and deployment
• 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

Qualifications

• 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