Senior Software Engineer

Role Overview

• 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

Requirements

• 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

Tech Stack

• Airflow
• Angular
• Apache
• AWS
• Cloud
• Docker
• EC2
• Kubernetes
• Microservices
• Postgres
• Python
• PyTorch
• React
• Scikit-Learn
• SDLC
• Spark
• SQL
• Tensorflow
• .NET

Benefits

• Health Insurance
• a Retirement Plan
• Disability benefits
• Paid Time Off