Job description

We are hiring a Senior Software Engineer with deep expertise in AI/ML engineering and data-intensive systems to join our Catastrophic and Risk Solutions team. You will be a key technical contributor on a cross-functional Agile team building cloud-native SaaS platforms that sit at the intersection of cutting-edge science and production software. This role goes beyond traditional full-stack development — you will design and ship AI-powered features, build data pipelines, and architect scalable ML-serving infrastructure on AWS. This role is office-based in our Boston location, which has a flexible hybrid work model. AI & Data Engineering • 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 Software Engineering & Architecture • 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 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 #LI-LM03 #LI-Hybrid We are hiring a Senior Software Engineer with deep expertise in AI/ML engineering and data-intensive systems to join our Catastrophic and Risk Solutions team. You will be a key technical contributor on a cross-functional Agile team building cloud-native SaaS platforms that sit at the intersection of cutting-edge science and production software.

Requirements

Must-haves

AI & Data Engineering
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
Software Engineering & Architecture
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
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

Nice-to-haves

B.S. in Computer Science, Mathematics, Statistics, or a related quantitative field; M.S. or Ph.D. preferred
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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlagileawsmachine learningllmpythonapisqlci/cdrestdockerkubernetesangularreact

Platform: oracle-cloud
Effort: medium