AI Engineer Senior/Lead

As a senior or lead AI engineer on the AI & Infrastructure team, you'll play a key role in advancing Progressive's enterprise AI capabilities by leading the design, development, and execution of Pygressive's Generative AI platform initiatives. We are looking for a hands-on technical leader with expertise in AI engineering, data infrastructure, and scalable platform design. In this role, you'll build foundational services, pipelines, and tools to enable secure, reliable, and impactful AI applications across the enterprise. You'll shape core components of the Generative AI stack, including prompt orchestration, vector storage, RAG pipelines, and model deployment tooling, while collaborating with engineers, data scientists, and product stakeholders to accelerate GenAI adoption, reduce operational friction, and deliver scalable platform solutions. You'll have the unique opportunity to lead both technically and strategically, helping to define how Progressive builds, scales, and governs AI across a federated and rapidly modernizing environment.

This is a remote position for US based work only.

Must-have qualifications

• Bachelor's Degree or higher in an Information Technology discipline or related field of study and minimum of two years of work experience designing, programming, and supporting software programs or applications.
• In lieu of degree, minimum of four years related work experience designing, programming, and supporting software programs or applications may be accepted.

Preferred Skills

• Proven experience building and scaling ML/AI systems in production, including LLMs, embeddings, RAG architectures, and NLP models, with hands-on expertise in LLM frameworks, vector databases, and embedding generation for search and retrieval use cases.
• Proficient in Python for developing APIs, ML services, and orchestration tools, with experience in containerized deployment using Docker and Kubernetes, and a strong understanding of orchestration systems and data pipelines using tools like Airflow, Prefect, Dagster, and Kubernetes for distributed, reliable processing at scale.
• Deep experience deploying and managing models with MLflow, SageMaker, Azure ML, or Vertex AI, including model monitoring, drift detection, and responsible AI practices.
• Skilled in infrastructure-as-code and GitOps workflows (Terraform, ArgoCD, GitHub Actions, Helm), with a strong focus on scalable, reusable platform components and best practices for platform engineering.
• Strong expertise in building robust, scalable systems with a focus on production-grade reliability, including monitoring, drift detection, and distributed processing at scale.

Compensation

• $114,900-$160,400/year depending on position level and experience
• Gainshare annual cash incentive