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About the role
8+ years of experience across software, data, platform, infrastructure, or AI engineering roles, including:
3+ years building LLM/AI applications such as RAG, agents, evaluations, workflow automation, or production AI systems.
5+ years working with Kubernetes and cloud-native infrastructure in production environments.
Strong experience with major cloud platforms such as AWS, Azure, or GCP.
Strong data engineering background, including pipelines, orchestration, transformation, data quality, access controls, and production data workflows.
Experience with a modern data stack such as Spark, Airflow, Databricks, Snowflake, or similar.
Experience building or deploying AI, data, or automation solutions in highly regulated or operationally complex industries, such as financial services, government, healthcare, energy, agriculture, supply chain, or industrial operations.
Ability to apply AI to real-world operational data, such as sensor data, geospatial data, logistics data, ERP data, field operations data, or forecasting data.
Proficiency in at least one production programming language such as Python, Go, TypeScript, Java, or Scala.
Strong systems thinking across data, users, permissions, workflows, infrastructure, governance, and business processes.
Excellent customer-facing communication skills with engineers, operators, security teams, executives, and business owners.
Strong ownership mindset: you care about production rollout, adoption, reliability, operational handoff, and measurable impact.
Willingness to travel to customer sites as needed.
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