Staff AI Engineer (Data & Intelligence function)
Acquia · Remote-United-States
About this role
**About Acquia** Acquia empowers the world’s most ambitious brands to create digital customer experiences that matter. With open source Drupal at its core, the Acquia Digital Experience Platform (DXP) enables marketers, developers, and IT operations teams to rapidly compose and deploy digital products and services that engage customers, enhance conversions, and help businesses stand out. Headquartered in Boston, MA, Acquia is a Great Place to Work-Certified™ company. --- **The Role: Staff AI Engineer (Data & Intelligence)** Acquia is seeking a **Staff AI Engineer** to join our **Data & Intelligence** function. Acquia runs one of the largest Drupal and digital experience footprints in the world and holds more than a decade of operational, product, and customer data. This role exists to put that data to work—building the retrieval, graph, and inference layers on top of the data platform, and the agentic and deterministic systems that turn signals into action for customers and internal teams. This is a **hands-on engineering role**: you’ll design, build, and ship production systems, and lift AI engineering capability through the quality of your work. --- **Key Responsibilities** - Write and ship **production AI code daily**. - Lead work to make **customer context retrievable at speed**, including semantic retrieval, indexing, and knowledge graph approaches. An early priority is enabling an agent to resolve a customer and pull full context in a single request. - Build the **inference and signal layer**: models and processors that read from an **Iceberg-based lakehouse**, write scored inference back, and expose signals through contracts other teams can build against. - Turn signals into **actionable workflows** (e.g., churn risk, usage/entitlement mismatch, expansion opportunities), with human review where appropriate. - Choose the right approach per workload: **large model**, **small fine-tuned model**, or **deterministic code**—and manage inference cost. - Own **evaluation and observability** for AI systems so signals are sound before anyone acts on them. - Set patterns other teams build against, and partner with engineering and business leaders to turn company goals into shipped systems. --- **How We Think About Experience** We’re more interested in how you learn than in tools listed on your CV. The stack spans **Python, SQL, dbt**, distributed query engines, durable workflow orchestration, an **Iceberg lakehouse on S3**, and a changing mix of model providers and agent frameworks. You should be comfortable closing gaps quickly—using AI to read unfamiliar codebases and contribute before you’re fully fluent. --- **Required Experience** - **8+ years** of software engineering, including **3+ years shipping AI/ML systems to production**. - Strong programming fundamentals and deep proficiency in at least one language used for AI/data work (**Python** is primary). - **Fluency working with data at scale**: advanced SQL, dbt (or similar), distributed query engines over lakehouse/warehouse storage, including reasoning about query cost and partitioning. - **Production retrieval and context engineering**: embeddings, vector search, hybrid/graph retrieval, and evaluation of retrieval quality; entity/relationship modeling across multi-source data is valuable. - **Agentic systems and durable workflows in production**: tool calling, state/memory, human-in-the-loop patterns (we use **Temporal** and **LangGraph**; equivalent experience transfers)
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