Description
We're looking for an experienced Senior Principal AI Engineer who will bring strategic leadership and deep subject-matter expertise in designing and implementing enterprise-scale AI/ML infrastructure, automation tools (MLOps and DevOps), and cloud-native AI solutions.
Responsibilities:
- Leading the design, architecture, and optimization of machine learning deployment tools and automation systems that operate the business's data and ML applications
- Defining and implementing best practices and standards for data and machine learning pipelines across the organization
- Collaborating with engineers and machine learning researchers to automate code analysis, build, integration, and deployment of ML applications
- Providing technical guidance on infrastructure design decisions and monitoring solutions
- Building highly scalable, resilient cloud and on-premise systems for hosting machine learning systems using state-of-the-art technologies, including Google Cloud Platform (GCP) and Vertex AI
- Mentoring junior engineers and contributing to team growth and technical excellence
Requirements:
- Extensive experience designing and implementing distributed ML systems at scale
- Hands-on experience with GCP services, particularly Vertex AI for model training, deployment, and monitoring
- Experience building and integrating Generative AI solutions, including LLMs, RAG architectures, and AI harnesses/orchestration frameworks (e.g., LangChain, LlamaIndex)
- Proficiency with MLOps orchestration tools such as AirFlow, KubeFlow, Dagster, or Flyte
- Experience with DevOps pipelines (Jenkins, GitHub Actions)
- Strong programming skills in Python; familiarity with Bash or JavaScript a plus
- Hands-on experience deploying hybrid environments across GCP, AWS, and/or Azure
- Familiarity with ML frameworks such as PyTorch or TensorFlow
- Knowledge of software engineering best practices including testing, code reviews, and source control
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