Staff AI Engineer (Data & Intelligence function)
Acquia, Inc. · USA
About this role
## About Acquia Acquia empowers ambitious brands to create digital customer experiences that matter. With open source **Drupal** at its core, the **Acquia Digital Experience Platform (DXP)** helps marketers, developers, and IT operations teams rapidly compose and deploy digital products and services. Acquia is building for the future—where AI agents become active participants in how people discover, consume, and act on digital content. ## The Role Acquia is seeking a **Staff AI Engineer** to join the **Data & Intelligence** function. You’ll help turn Acquia’s large operational, product, and customer datasets into action by building: - **Retrieval, graph, and inference layers** on top of the data platform - **Agentic and deterministic systems** that convert signals into workflows for customers and internal teams This is a **hands-on engineering** role focused on designing, building, and shipping production systems, and raising the AI engineering capability of teams through high-quality work. ## Key Responsibilities - **Write and ship production AI code daily** - Lead work on **customer context retrieval at speed**, including semantic retrieval, indexing, and knowledge graph approaches - Enable an agent to resolve a customer and pull full context in a **single request** - Build the **inference and signal layer** - Read from an **Iceberg-based lakehouse** - Write scored inference back - 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** - **Manage inference cost** - Own **evaluation and observability** so signals are trustworthy before action is taken - Set patterns other teams can build against and help translate company goals into shipped systems ## How We Think About Experience - We care more about **how you learn** than the exact tools on your CV - Stack spans **Python, SQL, dbt**, distributed query engines, durable workflow orchestration, an **Iceberg lakehouse on S3**, and a changing mix of model providers/agent frameworks - You should be comfortable picking up unfamiliar problems and closing gaps quickly (including using AI to read new codebases) ## Required Experience - **8+ years** 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** - transformation layer such as **dbt** - distributed query engines over lakehouse/warehouse storage - reasoning about **query cost and partitioning** - Production **retrieval and context engineering**: - embeddings, vector search, hybrid/graph retrieval - measuring whether retrieval is working - entity/relationship modeling across multi-source data is valuable - Production **agentic systems and durable workflows**: - tool calling, state/memory, human-in-the-loop patterns - **Temporal** and **LangGraph** (equivalent experience transfers) - **Evaluation and observability** for AI systems: - tracing, prompt/version management, dataset-driven testing - Cloud deployment experience ( **AWS preferred**), containerized services, and ownership of inference cost - **B.S. in Computer Sci
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