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AI Engineer Remote-United-States

Remote / Online - Candidates ideally in
Bowling Green, Warren County, Kentucky, 42101, USA
Listing for: Acquia
Remote/Work from Home position
Listed on 2026-09-11
Job specializations:
  • Software Development
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 145000 - 159000 USD Yearly USD 145000.00 159000.00 YEAR
Job Description & How to Apply Below
Position: Staff AI Engineer New Remote-United-States

Acquia is the digital experience platform built for a world where your audience isn't only human. Its agents too.

As AI agents become active participants in how people discover, consume, and act on digital content, Acquia gives enterprise teams the platform to create, manage, and distribute experiences designed for both. Powered by agentic AI that orchestrates — not just advises — Acquia automates complex digital workflows within the governance guardrails large organizations require.

The world's #1 Drupal hosting provider, Acquia brings together Content Management, Digital Asset Management, and Product Information Management in a single AI-powered Command Center:
Acquia Source.

Acquia. Built for every audience, human or otherwise.

The Role:

Acquia is seeking a Staff AI Engineer to join our AI Core Engineering team. This is first and foremost a hands-on engineering role — you will spend the majority of your time designing, building, and shipping production-grade agentic AI workflows across the Acquia DXP. Lang Graph, Temporal, Pydantic and Lang Fuse are your primary tools; enterprise reliability, observability, and scale are your standards.

You'll also play a light but meaningful mentoring role, helping to lift the AI engineering capability of those around you as the team grows.

Key Responsibilities
  • Write and ship production AI code daily — you are an active contributor.
  • Architect agentic AI workflows using Lang Graph, Temporal, Pydantic — stateful, multi-agent workflows built for enterprise scale and reliability.
  • Own AI observability via Lang Fuse: tracing, prompt versioning, evaluation, and performance benchmarking across all model interactions.
  • Set AI engineering standards for agent design patterns, RAG, prompt management, context optimization, and tool-calling strategies.
  • Partner with product and platform teams to deliver AI architectures that meet enterprise SLA, security, and compliance requirements.
  • Evaluate and adopt emerging tooling — benchmarking LLM providers, orchestration frameworks, and agentic stack improvements.
  • Mentor engineers as a natural extension of your work — sharing knowledge through code reviews, pairing sessions, and design discussions, not through management overhead.
  • Represent Acquia's AI capabilities in customer architectural reviews, technical discovery, and roadmap conversations.
Required Experience
  • 8+ years of software engineering with 3+ years in production of AI Agents.
  • Hands-on Lang Graph, Temporal, Pydantic expertise — stateful, cyclic, multi-agent workflows at enterprise scale.
  • Hands-on Lang Fuse expertise - tracing, evaluation, prompt management, and dataset- driven testing
  • Proficiency with agent harness frameworks such as Lang Chain or similar (e.g. Llama Index, CrewAI) — composing chains, tools, memory, and retrieval pipelines.
  • Deep Python proficiency and strong engineering fundamentals (testing, CI/CD, architecture).
  • Cloud AI deployment experience (AWS, Azure, or GCP) including containerization and inference cost management.
  • RAG architecture knowledge— vector databases, embedding models, and retrieval strategies.
  • B.S. in Computer Science or equivalent practical experience.
Desired Skills
  • Enterprise SaaS or CMS, including familiarity with Acquia's Drupal-based DXP experience
  • Agentic development workflow fluency — AI-assisted coding tools (Copilot, Cursor, Claude) as everyday accelerators.
  • Familiarity with persistent agent runtimes - such as Open Claw and Hermes Agent, understanding cross-session memory, autonomous skill creation, and always-on agent infrastructure as it matures in enterprise contexts.
  • LLM fine‑tuning or model evaluation experience and awareness of foundational model tradeoffs.
  • Human‑in‑the‑loop — interrupt‑driven agents and enterprise design
  • Strong…
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