AI Engineer IV

Premera Blue Cross · Insurance · Mountlake Terrace, WA

AI Engineer IV at Premera Blue Cross responsible for architecting and delivering AI systems end-to-end, leading ideation and rapid validation, shaping cloud architecture for AI platforms, enabling robust data foundations, operationalizing AI via services and APIs, building monitoring and reliability into production, driving engineering excellence, documenting with production discipline, mentoring and raising the bar, influencing strategy and roadmaps, partnering with stakeholders and upholding governance, and providing thought leadership. Requires a Bachelor's degree or equivalent experience in a related field, with at least 8 years of combined software development and AI/ML systems experience. Preferred qualifications include experience in regulated environments, productionizing AI models, deep learning frameworks, software design patterns, and ethical AI practices.

What you'd actually do

  1. Architect and deliver AI systems end‑to‑end.
  2. Lead ideation and rapid validation.
  3. Shape cloud architecture for complex AI platforms.
  4. Enable robust data foundations.
  5. Operationalize AI via services and APIs.

Skills

Required

  • software development lifecycle
  • proficiency in multiple programming languages
  • developing, deploying, and maintaining AI/ML systems

Nice to have

  • Azure AI services
  • healthcare experience
  • scalable pipelines
  • robust monitoring systems
  • TensorFlow
  • PyTorch
  • MLX
  • software design patterns
  • microservices
  • distributed systems
  • container orchestration
  • ethical AI practices
  • explainability
  • fairness
  • bias mitigation
  • LLM solution patterns
  • retrieval-augmented generation
  • structured reasoning approaches
  • transformers
  • CNNs
  • GANs
  • LSTMs
  • GNNs
  • autoencoders
  • diffusion models
  • NODEs
  • debug and optimize AI systems
  • performance analysis
  • tuning approaches
  • secure, stable systems at scale
  • AI system design and deployment patterns
  • stakeholder influence
  • explain technical tradeoffs clearly to non-technical audiences
  • mentorship
  • leadership mindset

What the JD emphasized

  • highly regulated environment
  • production deployment
  • productionizing AI models
  • production discipline
  • production

Other signals

  • end-to-end AI systems
  • prototypes into production
  • cloud architecture for AI platforms
  • scalable data pipelines
  • low-latency APIs/services
  • monitoring models and AI systems
  • productionizing AI models
  • LLM solution patterns