Software Engineer - AI/ML Platform Operations - Hybrid

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 Software Engineer - AI/ML Platform Operations - Hybrid. Selected candidate will be expected to work 1-2 days per month from our Phoenix office. Your Role: We are seeking a Software Engineer – AI/ML Platform Operations to lead the operational excellence, reliability, and support of our enterprise AI and data platforms. This role is responsible for ensuring the stability, scalability, observability, governance, and operational readiness of AI/ML solutions that power critical business capabilities. This is not a traditional software application development role. While strong software engineering skills are essential, the primary focus is on AI platform operations, MLOps, automation, reliability engineering, deployment support, observability, governance, and continuous improvement of enterprise AI capabilities. Your Work: You will work across a modern technology ecosystem that includes Palantir Foundry, AWS Bedrock, Amazon SageMaker, cloud-native services, and emerging Generative AI technologies. You will partner with Data Engineering, Data Science, Architecture, Infrastructure, Security, and Product teams to support production AI workloads and enable the successful adoption of AI capabilities across the organization. AI Platform Operations & Reliability • Provide technical leadership for AI/ML platforms including Palantir, AWS Bedrock, Amazon SageMaker, and related cloud-native technologies. • Ensure platform reliability, scalability, performance, security, and operational readiness for production AI workloads. • Support deployment, monitoring, maintenance, and lifecycle management of AI/ML solutions and Generative AI services. • Establish operational standards, support models, service-level objectives (SLOs), and platform governance practices. MLOps, Automation & Observability • Design and implement automation, monitoring, observability, and operational tooling to improve platform reliability and efficiency. • Develop and maintain dashboards, health metrics, alerts, logging frameworks, and operational runbooks. • Enhance CI/CD pipelines, deployment automation, infrastructure-as-code, and model release processes. • Implement best practices for MLOps, model monitoring, model lifecycle management, and AI operational governance. Incident Management & Problem Resolution • Serve as a senior escalation point for complex production issues involving AI platforms, machine learning workloads, cloud infrastructure, and data integrations. • Lead root cause analysis efforts and drive corrective and preventive actions to improve platform stability. • Solve performance, availability, deployment, and integration issues across AI and data ecosystems. • Partner with engineering and business teams to rapidly restore service and minimize operational risk. Technical Leadership & Collaboration • Provide mentorship, technical guidance, and operational expertise to engineers and platform teams. • Influence platform strategy, architecture decisions, operational processes, and technology adoption. • Collaborate with team members to align platform capabilities with business priorities and AI adoption goals. • Communicate complex technical concepts effectively to both technical and non-technical audiences. Continuous Improvement & Innovation • Remain current with advancements in AI/ML, Generative AI, cloud technologies, platform engineering, and reliability practices. • Identify opportunities to improve operational efficiency, governance, security, and developer experience. • Champion modern engineering practices including automation, observability, DevOps, Site Reliability Engineering (SRE), and AI Operations (AIOps). Required Experience, Education and Skills • 4+ years of progressive experience in software engineering, platform engineering, cloud operations, MLOps, DevOps, or related technical disciplines. • Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience. • Experience supporting production cloud-based applications and services in AWS environments. • Strong experience with software engineering and automation using languages such as Python, Java, JavaScript/TypeScript, or Node.js. • Experience with CI/CD, build, integration, and deployment tools such as Jenkins, Maven, GitHub Actions, or equivalent. • Experience with cloud-native services including compute, storage, networking, databases, and serverless architectures. • Experience building and maintaining operational monitoring, observability, and alerting solutions. • Strong troubleshooting, incident response, and root cause analysis skills. • Excellent communication, collaboration, and technical leadership capabilities. What would make us excited about you? • Fulfills business needs, which may include investing extra time, helping other teams, etc • Experience with AI/ML platforms such as Palantir Foundry, Amazon SageMaker, AWS Bedrock, Databricks, or similar ecosystems. • Experience supporting Generative AI applications, LLM-based solutions, prompt orchestration frameworks, and Retrieval-Augmented Generation (RAG) architectures. • Knowledge of MLOps practices including model deployment, monitoring, governance, experimentation, and lifecycle management. • Experience with observability and monitoring platforms such as Datadog, Splunk, Grafana, Prometheus, CloudWatch, or OpenTelemetry. • Familiarity with AI governance,