Staff AI Engineer (Data & Intelligence function)
About this role
Acquia needs to put a decade of operational, product, and customer data to work by building the retrieval, graph, and inference layers that turn signals into automated actions for enterprise teams. Operating within the Data and Intelligence function, the Staff AI Engineer designs and ships production-grade systems across observability, core data platforms, and insights while collaborating closely with product and platform teams. The person taking on this position will write production code daily, manage inference costs, and establish engineering patterns for agentic and deterministic workflows.
Success in the role requires an adaptable engineer who thrives in a hands-on environment where scope shifts with company priorities. Candidates should be comfortable picking up unfamiliar technologies quickly, utilizing AI-assisted coding tools to bridge gaps in the stack, and elevating the overall AI capability of neighboring teams through technical excellence rather than management tasks.
What you will do
- Write and ship production AI code daily as an active contributor to the engineering team.
- Lead initiatives for fast customer context retrieval across semantic retrieval, indexing, and knowledge graph approaches.
- Build inference and signal layers that read from an Iceberg-based lakehouse and expose scored signals through contracts for other teams.
- Turn detected business conditions into automated workflows with human-in-the-loop review designs.
- Determine the right tool for each workload by balancing large models, small fine-tuned models, and ordinary deterministic code while managing inference costs.
- Own evaluation and observability for AI systems to verify signal reliability before operational use.
- Establish patterns that other teams build against while partnering with leaders to translate company goals into shipped systems.
What you need
- Bring 8+ years of software engineering experience, including at least 3 years shipping AI or ML systems to production.
- Demonstrate strong programming fundamentals and deep proficiency in Python or another language used for AI and data work.
- Show fluency working with data at scale using advanced SQL, dbt, and a distributed query engine over lakehouse or warehouse storage.
- Possess production experience in retrieval and context engineering, covering embeddings, vector search, and hybrid or graph retrieval.
- Have hands-on production background with agentic systems, durable workflows, tool calling, state and memory, and human-in-the-loop patterns.
- Maintain practical knowledge of evaluation and observability for AI systems, such as tracing, version management, and dataset-driven testing.
- Hold cloud deployment experience with containerized services on AWS and demonstrate ownership of inference costs.
- Possess a B.S. in Computer Science or equivalent practical experience.
Nice to have
- Experience standing up shared data or ML capability for other teams to build upon
- Small and fine-tuned model work including distilling task-specific models
- Observability data fluency with OpenTelemetry, logs, metrics, and traces at high volume
- Data governance instincts around access control, lineage, and data residency
- Familiarity with enterprise SaaS, CMS, or Acquia's Drupal-based DXP
Benefits
- Competitive healthcare coverage
- Wellness programs
- Take it when you need it time off
- Parental leave
- Recognition programs
Skills and tech stack
Questions candidates ask
What is the remote work policy for this position?
The role is designated as remote within the United States.
What is the salary range for this job?
The salary range for this position is $150,000 to $200,000 USD.
What tools and frameworks make up the technology stack?
The stack spans Python, SQL and dbt, distributed query engines, durable workflow orchestration, an Iceberg lakehouse on S3, and a changing mix of model providers and agent frameworks.
What does the recruitment process involve?
The recruitment process is designed to empower candidates in making informed decisions, providing an inclusive, transparent, efficient, and educational interview experience.