Staff AI Engineer

Acquia

2d•$150,000 - $200,000

About The Position

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 across it. This role exists to put that data to work: building the retrieval, graph, and inference layers on top of our data platform, and the agentic and deterministic systems that turn the resulting signals into action for our customers and our teams. The remit is broad. Our function spans observability, our core data platform, and insights and intelligence, and you will work across all three alongside the product and platform teams that consume what we build. Scope moves with company priorities, so we are looking for someone comfortable changing context and picking up an unfamiliar problem. Much of what you build will be shared capability that several teams depend on. This is a hands-on engineering role. You will spend most of your time designing, building, and shipping production systems, and you will lift the AI engineering capability of the teams around you through the quality of your work rather than through management overhead. We ship early and iterate.

Requirements

8+ years of software engineering, including 3+ years shipping AI or ML systems to production.
Strong programming fundamentals and deep proficiency in at least one language used for AI and data work. Python is our primary language for ML and insights.
Fluency working with data at scale — advanced SQL, a transformation layer such as dbt, and a distributed query engine over lakehouse or warehouse storage, including reasoning about query cost and partitioning.
Production retrieval and context engineering — embeddings, vector search, hybrid or graph retrieval, and measuring whether retrieval is actually working. Experience modelling entities and relationships across multi-source data is valuable here.
Agentic systems and durable workflows in production, with tool calling, state and memory, and human-in-the-loop patterns. We use Temporal and LangGraph; equivalent experience transfers.
Evaluation and observability for AI systems — tracing, prompt and version management, and dataset-driven testing, whatever the tooling.
Cloud deployment experience (AWS preferred) with containerized services, and ownership of inference cost.
B.S. in Computer Science or equivalent practical experience.

Nice To Haves

Experience standing up shared data or ML capability that other teams then built on.
Small and fine-tuned model work, including distilling task-specific models to replace general-model calls at scale.
Observability data fluency: OpenTelemetry, and logs, metrics and traces at very high volume, including what it takes to make that data queryable.
Data governance instincts around access control, lineage, and data residency, treated as part of the design.
Enterprise SaaS or CMS, including familiarity with Acquia's Drupal-based DXP.
Agentic development workflow fluency: AI-assisted coding tools (Claude, Cursor, Copilot) as everyday accelerators, and MCP or similar for connecting agents to real systems.
Strong communication skills — able to present AI system design to both engineers and C-suite stakeholders.
Senior IC track record — known for the quality of your own code and system designs, with mentoring that happens organically through great work, not through meetings.

Responsibilities

Write and ship production AI code daily. You are an active contributor.
Lead our work on making customer context retrievable at speed, across semantic retrieval, indexing, and knowledge graph approaches over our data platform. An early priority is letting an agent resolve a customer and pull its full context in a single request. Where that work goes from there is partly yours to define.
Build the inference and signal layer: models and processors that read from our Iceberg-based lakehouse, write scored inference back, and expose signals through contracts other teams can build against.
Take signals through to action, turning detected conditions such as churn risk, usage and entitlement mismatch, or expansion opportunity into workflows that act, with human review where that is the right design.
Choose the right tool for each workload: a large model, a small fine-tuned model, or ordinary deterministic code. Managing inference cost is part of the job.
Own evaluation and observability for our AI systems, so we can show a signal is sound before anyone acts on it.
Set the patterns other teams build against, and work with engineering and business leaders to turn company goals into shipped systems.