AI Engineer

Mutual of Omaha is seeking an experienced AI Engineer to lead the design and implementation of Machine Learning and generative AI applications for their Workplace Solutions division. The role involves driving AI strategy, collaborating with business leadership, and operationalizing AI to enhance customer experiences and optimize operations.

Responsibilities:

• Design, develop, and deploy AI and generative AI solutions, leveraging large language models to build and enable analytics, machine learning capabilities, and intelligent automation across the division
• Collaborate with business leadership and enterprise architects to define a roadmap for ML and generative AI capabilities (e.g., LLM application patterns), focusing on platform modernization and governance aligned to measurable outcomes
• Build and iterate on AI/ML and generative AI proofs of concept (POCs), data/feature pipelines, and model workflows. Facilitate 'what-if' exploration, whiteboarding, and design sprints to validate feasibility and business impact. Own solutions end-to-end from prototype through production rollout
• Establish and uphold standards for quality, governance, ethical AI practices, and secure model deployment while promoting regulatory compliance and trust. Improve accuracy by employing systematic experimentation, prompt engineering, data and training versioning, and A/B testing to empirically optimize model performance. Implement AI Ops best practices to streamline the lifecycle of AI models
• Guide teams in adopting and building LLM-powered applications, including context engineering, orchestration patterns, and evaluation practices. Accelerate adoption by providing reusable templates and reference implementations that enable consistent, scalable delivery and stay current on advancements in GenAI/AI, cloud capabilities, and emerging best practices
• Integrate data solutions with microservices, event-based architectures, and external business partners, ensuring scalability, observability, and operational readiness

Requirements:

• Bachelor's degree in Science, Engineering, or quantitative fields (Mathematics, Statistics, Data Science, etc.), or equivalent experience, with at least 5 years deploying and supporting full-stack applications in enterprise environments, and several years of experience integrating AI, ML solutions into full-stack applications
• Experienced in full-stack development (backend, frontend, and database technologies) with expertise in frameworks like React or Vue, and strong skills in Python, TypeScript, git, SQL, CI/CD pipelines, automated testing, and DevOps best practices
• Experienced in AWS services, particularly Bedrock, SageMaker, S3, Lambda, and infrastructure as code (CDK). Extensive experience applying software engineering design patterns and enterprise application architecture principles to build secure, scalable, maintainable, and cost-optimized cloud-native applications
• Experienced in developing and deploying AI/ML systems, with expertise in GenAI application development, LLM integration, and AIOps, implementing responsible AI practices, and optimizing AI solutions for production performance
• Strong communicator and collaborator, experienced at building partnerships in remote and ever-changing environments. Resilient and resourceful problem solver, proactive at overcoming obstacles to achieve goals. Comfortable with ambiguity and change, demonstrating high learning agility, adapting quickly to shifting priorities, and bringing clarity to undefined problems
• You promote a culture of diversity and inclusion, value different ideas and opinions, and listen courageously, remaining curious in all that you do
• Able to work remotely with access to a high-speed internet connection and located in the United States or Puerto Rico
• Group Benefits or insurance industry experience
• Advanced degree in an analytical field with proven in-depth AI knowledge
• Experienced with AI/ML OPS practices, distributed data processing, and building real-time data pipelines and familiar with data mesh principles, and real-time analytics
• Familiarity with single or multi-cloud agentic architecture for building LLM-based applications
• AI engineer who can design and implement production-grade solutions across probabilistic and deterministic approaches (LLMs/classical ML/rules), including evaluation, orchestration, and integration into maintainable services