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About This Role
About Acquia
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. LangGraph, Temporal, Pydantic and LangFuse 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 LangGraph, Temporal, Pydantic — stateful, multi\-agent workflows built for enterprise scale and reliability.
- Own AI observability via LangFuse: 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 LangGraph, Temporal, Pydantic expertise — stateful, cyclic, multi\-agent workflows at enterprise scale.
- Hands\-on LangFuse expertise \- tracing, evaluation, prompt management, and dataset\- driven testing
- Proficiency with agent harness frameworks such as LangChain or similar (e.g. LlamaIndex, 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 OpenClaw 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 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.
*We are an organization that embraces innovation and the potential of AI to enhance our processes and improve our work. We are always looking for individuals who are open to learning new technologies and collaborating with AI tools to achieve our goals.*
*Acquia is proud to provide best\-in\-class benefits offerings to our employees and their families in maintaining both a healthy body and a healthy mind. Core Benefits include: competitive healthcare coverage, wellness programs, take it when you need it time off, parental leave, recognition programs, and much more!*
*Acquia is an equal opportunity (EEO) employer. We hire without regard to age, color, disability, gender (including gender identity), marital status, national origin, race, religion, sex, sexual orientation, veteran status, or any other status protected by applicable law. For more information about our commitment to inclusivity and diversity, please visit our* *Diversity and Inclusion page**.*
Salary Context
This $180K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Acquia, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($205K) sits 13% above the category median. Disclosed range: $180K to $230K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Acquia AI Hiring
Acquia has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Boston, MA, US. Compensation range: $230K - $230K.
Location Context
AI roles in Boston pay a median of $215,350 across 442 tracked positions. That's 8% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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