Software Engineer III - AI/ML Platform Operations - Remote

CSAA Insurance Group is a AAA insurer seeking a Software Engineer III to lead the operational excellence, reliability, and support of enterprise AI and data platforms. The role focuses on AI/ML platform operations, MLOps, automation, observability, governance, deployment support, incident resolution, and continuous improvement across technologies including Palantir Foundry, AWS Bedrock, and Amazon SageMaker.

Responsibilities:

• 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
• 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
• 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
• 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
• 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)

Requirements:

• 3+ 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
• Applicants must have authorization to work indefinitely in the US
• Please do not apply for this role if at any time (now or in the future) you will need immigration support (i.e., H-1B, TN, STEM OPT Training Plans, 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, responsible AI principles, model risk management, and operational controls
• Relevant cloud, AI/ML, DevOps, or platform engineering certifications