About this role
Engineers who thrive in ambiguity and enjoy building foundational capabilities that multiple teams depend on will find a natural fit in this role. Acquia is hiring a Staff AI Engineer to join the Data and Intelligence function and put a decade of operational and product data to work through scalable retrieval, graph, and inference layers.
Working closely with product and platform teams, this person will design and ship production systems that turn complex data into agentic and deterministic actions. The scope covers everything from observability and core data platforms to insights, requiring someone who can context-switch easily and adapt as company priorities evolve.
This is a hands-on engineering position focused on writing production code daily, optimizing inference costs, and establishing architectural patterns for the wider engineering organization.
What you will do
- Write and ship production AI code daily as an active contributor to the Data and Intelligence function.
- Build retrieval systems that let agents resolve customers and pull full context rapidly using semantic search, indexing, and knowledge graphs.
- Develop inference and signal layers that read from an Iceberg lakehouse, write back scored predictions, and expose data contracts to other teams.
- Create automated workflows that turn detected conditions like churn risk or expansion opportunities into actions, incorporating human review where necessary.
- Select the most appropriate tool for each workload, balancing large models, fine-tuned smaller models, and ordinary deterministic code while managing inference costs.
- Own evaluation and observability for AI systems to verify signal reliability before deployment.
- Establish architectural patterns and collaborate with engineering and business leaders to align technical work with company goals.
What you need
- Over 8 years of software engineering experience, with at least 3 years spent shipping AI or ML systems to production.
- Strong programming fundamentals and deep proficiency in Python or another language used for AI and data work.
- Fluency working with data at scale, including advanced SQL, transformation layers like dbt, and distributed query engines over lakehouse or warehouse storage.
- Practical experience with production retrieval and context engineering, covering embeddings, vector search, hybrid or graph retrieval, and measuring retrieval performance.
- Demonstrated experience building agentic systems and durable workflows in production with tool calling, state management, and human-in-the-loop patterns.
- Familiarity with evaluation and observability practices for AI systems, such as tracing, prompt management, and dataset-driven testing.
- Cloud deployment experience using containerized services on AWS with an understanding of inference cost ownership.
- Possession of 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 to replace general-model calls
- Observability data fluency with OpenTelemetry, logs, metrics, and traces at high volume
- Data governance instincts covering 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
Where is this job located?
This is a remote position based in Canada.
What tools and technologies will I be working with?
The tech 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 will I spend most of my time doing?
This is a hands-on engineering role where you will spend most of your time designing, building, and shipping production systems.
What kind of background is required for this role?
Candidates need over 8 years of software engineering experience, including 3 or more years focused on shipping AI or machine learning systems to production.