Machine Learning Engineer - Remote

CSAA Insurance Group, a AAA Insurer•Washington, DC

2d•Remote

About The Position

CSAA Insurance Group (CSAA IG), a AAA insurer, is one of the leading personal lines property and casualty insurance groups in the United States. Here, every employee shapes our mission. We build innovative, human-centered solutions that help AAA members prevent, prepare for, and recover from life's uncertainties. You will join a collaborative, inclusive culture where your strengths have room to grow and your ideas can drive real impact. Step into a role where you can contribute to our shared success through meaningful work. We are actively hiring for a Machine Learning Engineer - Remote Your Role: We are seeking a skilled and motivated AI/ML Engineer to join our Data & AI Platform Engineering team. In this role, you will contribute to the design, implementation, and support of AI and ML capabilities that operate as part of an enterprise data and AI platform. You will work closely with senior engineers, data scientists, and cross-functional partners to deliver high-quality AI/ML components that are production-ready, well-governed, and aligned with established engineering standards. This role requires strong software engineering fundamentals, experience working with unstructured data and metadata, and an understanding of how semantic modeling can improve how users discover and interact with data and AI solutions. You will also stay current with evolving AI technologies, including Large Language Models (LLMs) and foundation model platforms, and apply them responsibly within a governed enterprise environment.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
3+ years of experience in AI/ML engineering or applied machine learning.
Experience delivering AI/ML solutions that are used in production environments.
Hands-on experience working with unstructured data, embeddings, or semantic techniques.
Strong programming skills, particularly in Python; experience with Java or similar languages is a plus.
Experience with AI/ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
Experience deploying AI/ML workloads on cloud platforms, preferably AWS and AWS Bedrock
Experience with data processing technologies such as Spark, SQL, or NoSQL systems.
Familiarity with Palantir (Foundry, AIP, or related tools) is required.

Nice To Haves

Strong problem-solving and analytical skills with attention to detail.
Effective communication and collaboration skills in cross-functional teams.
Ability to operate in an agile, fast-paced engineering environment.
Desire to learn, grow, and deepen expertise in AI/ML engineering and platform development.
Actively shapes our company culture (e.g., participating in employee resource groups, volunteering, etc.)
Lives into cultural norms (e.g., willing to have cameras when it matters: helping onboard new team members, building relationships, etc.)
Travels as needed for role, including divisional / team meetings and other in-person meetings
Fulfills business needs, which may include investing extra time, helping other teams, etc

Responsibilities

Design and Implementation Design, implement, and support AI and ML components under the guidance of senior engineers and architects.
Build AI/ML solutions that are secure, stable, testable, and maintainable, following established platform and engineering patterns.
Apply existing engineering and AI/ML frameworks to ensure consistency, scalability, and operational readiness.
Evaluate tradeoffs between performance, scalability, cost, and usability when implementing solutions.
Unstructured Data, Metadata, and Semantics Implement pipelines to ingest, process, and enrich unstructured and semi-structured data such as documents and text.
Work with metadata, embeddings, and semantic representations to improve search, retrieval, and downstream AI use cases.
Apply semantic modeling techniques to help make data more discoverable, interpretable, and useful to end users and applications.
Code Quality and Engineering Practices Write high-quality, production-grade code using modern software engineering best practices.
Contribute to automated testing, code reviews, and CI/CD pipelines for AI/ML systems.
Turn well-defined designs into working software and deliver on commitments reliably.
Problem Solving and Collaboration Implement solutions to moderately complex problems by understanding requirements, constraints, and business context.
Collaborate with data scientists, platform engineers, and business partners to deliver effective AI/ML capabilities.
Contribute to user stories, technical documentation, and design discussions.
Automation and Operational Excellence Support MLOps / FMOps practices such as model deployment, versioning, monitoring, and troubleshooting.
Identify operational issues and work with the team to improve reliability, observability, and maintainability.
Contribute improvements to existing pipelines, tools, and platform components.
LLMs and Foundation Models Apply LLMs and prompt engineering techniques to business use cases under established architectural and governance guidelines.
Contribute to solutions using managed model platforms such as AWS Bedrock.
Assist in evaluating and integrating new AI capabilities into the platform.
Data and AI Governance Build AI/ML solutions that adhere to data and AI governance standards, including documentation, lineage, and testing requirements.
Partner with governance and compliance teams to ensure AI/ML implementations meet enterprise expectations.
Follow established practices for model versioning, validation, and transparency.