Staff AI Engineer

Accountabilities

• • Design, build, and ship production-grade AI solutions and agentic workflows as an active hands-on engineering contributor.
  • Architect stateful, cyclic, and multi-agent workflows using LangGraph, Temporal, and Pydantic, with a focus on enterprise reliability, scalability, and maintainability.
  • Establish and improve AI observability practices using LangFuse, including tracing, prompt versioning, evaluation, dataset-driven testing, and performance benchmarking.
  • Define engineering standards and reusable patterns for agent design, retrieval-augmented generation (RAG), prompt management, context optimization, memory, and tool-calling strategies.
  • Collaborate with product and platform teams to develop AI architectures that satisfy enterprise-level SLA, security, compliance, and reliability requirements.
  • Evaluate emerging AI technologies, LLM providers, orchestration frameworks, and agentic development tools through structured benchmarking and experimentation.
  • Contribute to the continuous improvement of AI engineering practices, development workflows, testing approaches, and production operations.
  • Mentor other engineers through code reviews, technical pairing, architecture discussions, and knowledge sharing, without taking on traditional people-management responsibilities.
  • Participate in customer-facing architectural reviews, technical discovery sessions, and roadmap discussions, clearly communicating AI capabilities and technical trade-offs.

  Requirements

  • 8+ years of professional software engineering experience, including at least 3 years building and operating AI agents in production environments.
  • Strong hands-on expertise with LangGraph, Temporal, and Pydantic, particularly for stateful, cyclic, and multi-agent workflows at enterprise scale.
  • Hands-on experience with LangFuse or comparable AI observability platforms, including tracing, evaluation, prompt management, and dataset-driven testing.
  • Experience with agent orchestration frameworks such as LangChain, LlamaIndex, CrewAI, or similar technologies, including chains, tools, memory, and retrieval pipelines.
  • Deep proficiency in Python and strong software engineering fundamentals covering architecture, automated testing, CI/CD, code quality, and production reliability.
  • Experience deploying AI systems in AWS, Azure, or GCP, including containerization and management of inference and infrastructure costs.
  • Strong understanding of RAG architectures, vector databases, embedding models, retrieval strategies, and related AI system design considerations.
  • Bachelor's degree in Computer Science or equivalent practical engineering experience.
  • Familiarity with enterprise SaaS or content management platforms is an asset, as is experience with Drupal-based digital experience ecosystems.
  • Fluency with AI-assisted development tools such as GitHub Copilot, Cursor, Claude, or comparable technologies, with the ability to incorporate AI into everyday engineering workflows.
  • Exposure to persistent agent runtimes, cross-session memory, autonomous agent capabilities, and always-on agent infrastructure is a plus.
  • Experience with LLM fine-tuning, model evaluation, foundational model trade-offs, and human-in-the-loop agent architectures is desirable.
  • Strong communication skills, with the ability to explain sophisticated AI architectures and technical trade-offs to engineers, product leaders, and executive stakeholders.
  • Demonstrated senior individual-contributor experience, with a reputation for high-quality code, strong system designs, technical ownership, and organic mentorship.
  • An AI-native mindset, strong adaptability, intellectual curiosity, and a builder mentality, with a willingness to continuously rethink traditional workflows through AI and automation.

  Benefits

  • Competitive healthcare coverage designed to support employees and their families.
  • Wellness programs supporting physical and mental well-being.
  • Flexible paid time off that can be used when needed.
  • Parental leave benefits.
  • Employee recognition programs.
  • Opportunities to work with advanced AI technologies and emerging agentic architectures.
  • Professional growth through exposure to complex enterprise-scale engineering challenges.
  • A collaborative environment that values innovation, continuous learning, and responsible adoption of AI.
  • Equal opportunity and an inclusive workplace committed to providing fair access to employment opportunities and resources.