AI Engineer III

Premera Blue Cross · Insurance · Telecommuter

AI Engineer III role focused on taking AI/ML solutions from concept to production implementation within a healthcare company. Responsibilities include developing systems and frameworks, prototyping, productionizing AI using cloud resources (Azure), building data pipelines, creating low-latency APIs for model deployment, and monitoring AI systems in production. Requires strong software engineering abilities and experience with ML algorithms.

What you'd actually do

  1. Develop comprehensive systems and frameworks for AI applications and products.
  2. Construct prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources.
  3. Develop, refine, and productionize AI systems using cloud resources such as Azure OpenAI, Azure ML, and Cognitive Services.
  4. Contribute to the creation of robust data pipelines for model evolution and production systems.
  5. Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.

Skills

Required

  • Bachelor's Degree in Computer Science, Statistics, Mathematics, or a related field, or 2+ years of experience in a related, professional IT/analytics position.
  • Minimum of 3 years of industry experience in developing, deploying, and maintaining AI or ML systems.
  • Proficiency in debugging AI systems and enhancing performance through hyperparameter tuning and similar techniques.
  • Proficient software engineering skills as well as skills building secure, stable software systems at scale.
  • Proficiency in developing and optimizing ML solutions using languages like Python, and libraries such as NumPy, Pandas, Matplotlib and scikit-learn.
  • Familiarity working with traditional ML lifecycles.

Nice to have

  • At least 4 years of experience in developing deep learning models using TensorFlow, PyTorch, MLX, JAX, or other modern deep learning frameworks.
  • Knowledge of and experience in implementing ethical AI practices, with at least 2 years spent working on projects that require explainable AI, fairness, and bias mitigation.
  • Minimum of 3 years of proficiency in utilizing advanced prompt engineering techniques like General Knowledge Prompting and ReAct.
  • Adept at using libraries like LangChain or OpenPrompt for complex projects.
  • Possess the capability to mitigate prompt injection attacks and use tools like Guardrails or PromptInject for securing prompt engineering pipelines.
  • Minimum of 3 years of experience in successfully productionizing AI models, including constructing scalable data pipelines and establishing robust monitoring systems.
  • 3 or more years working within Agile-like teams and environments.
  • Experience developing deep learning architectures such as CNNs, transformers, GANs, LSTMs, GNNs, Autoencoders, Diffusion Models, and Neural Ordinary Differential Equations (NODEs).
  • Familiarity with software design patterns, microservices, distributed computing, container orchestration, and other relevant architectures.
  • Familiarity with building minimal interfaces to interact with AI products, e.g., Streamlit or Shiny.
  • Familiarity with ethical AI practices including explainable AI, fairness, and mitigation of bias/hallucinations.
  • Strong communication, collaboration, and mentorship skills.
  • Growing ability to articulate the technical details and tradeoffs of AI solutions to non-technical stakeholders in a clear and concise manner.

What the JD emphasized

  • Minimum of 3 years of industry experience in developing, deploying, and maintaining AI or ML systems.
  • Minimum of 3 years of proficiency in utilizing advanced prompt engineering techniques like General Knowledge Prompting and ReAct.
  • Minimum of 3 years of experience in successfully productionizing AI models, including constructing scalable data pipelines and establishing robust monitoring systems.

Other signals

  • Develop comprehensive systems and frameworks for AI applications and products.
  • Develop, refine, and productionize AI systems using cloud resources such as Azure OpenAI, Azure ML, and Cognitive Services.
  • Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.
  • Supervise and sustain AI systems in production to guarantee consistent model accuracy and reliability.