Level

Cloudera

Forward Deployed AI Engineer (Senior/Principal Level)

Cloudera is hiring a Forward Deployed AI Engineer (Senior/Principal Level) for a remote role open to applicants in Ireland. Level rates it ; you can apply on Level.

AI in this role

ragai-agentsfine-tuningml-ops

Business Area:

Corp. Strategy

Seniority Level:

Director

Job Description: 

At Cloudera, we empower people to transform complex data into clear and actionable insights. With as much data under management as the hyperscalers, we're the preferred data partner for the top companies in almost every industry.  Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world’s largest enterprises.

About Forward Deployed Engineering (FDE) at Cloudera

Cloudera’s Forward Deployed Engineering (FDE) function sits within the Applied AI organization, and is a specialized AI/ML engineering team focused on accelerating AI adoption within our most strategic enterprise customers. We embed directly with customers to rapidly pilot AI use cases, solve real-world business challenges, and help operationalize advanced AI capabilities within enterprise environments. 

Cloudera FDEs operate at the intersection of customer engineering, AI innovation, and enterprise deployment - driving high-impact AI outcomes for customers and codifying solution patterns for repeatable delivery at scale.

About the role

Cloudera is looking for a seasoned full-stack ML engineer who can embed with customers to design technical use cases, and ship scalable enterprise AI/ML solutions on Cloudera. As a Forward Deployed AI Engineer you will:

  • Build & Prototype AI Apps - Develop full-stack AI/ML applications on Cloudera to demonstrate how AI and agentic systems solve real enterprise use cases

  • Partner with Strategic Customers - Work directly with customer teams to drive AI and agentic use cases from early prototype toward production readiness

  • Shape Enterprise AI Architecture - Advise customer leaders on AI roadmaps, solution design, deployment, and AI use cases

  • Codify Solution Patterns - Standardize successful patterns into repeatable reference architectures, productized solutions, starter kits, and internal playbooks 

  • Multiply impact - Be a thought leader, mentor Cloudera teams, and contribute to resources that grow our AI capability. Channel field and customer signal back to Product teams to improve our AI platform and products

We are excited if you have:

  • 7+ years of experience building and deploying production-grade systems, including 2-4 years experience building ML systems or GenAI/agentic applications

  • Strong hands-on software engineering, data engineering, and applied AI/ML skills, with experience building full-stack ML applications or agentic systems using modern AI frameworks and tooling

  • Experience with foundation models, context engineering, fine-tuning, semantic search, Retrieval-Augmented Generation (RAG), and agentic workflows

  • Deep understanding of LLMOps/MLOps practices including evaluation, observability, serving, monitoring, and lifecycle management

  • Experience designing and implementing scalable AI solution architectures aligned to enterprise standards (security, governance, compliance, etc.)

  • The ability to engage directly with customers, both technical and executive audiences - to translate needs into actionable technical strategy

  • High agency with the ability to operate autonomously and thrive in ambiguous, fast-moving customer environments

  • A collaborative mindset and passion for enabling others through mentorship, internal enablement, or reusable tooling

  • Bsc/Msc/PhD in related field or equivalent experience

  • This is a customer-facing role combining remote and in-person engagement. Occasional travel may be required for on-site customer engagement, industry events, and team meetings - up to 20% depending on business needs.

You may also have:

  • Experience with cloud technologies (AWS, Azure, GCP)

  • Experience using the Cloudera platform 

  • Experience with open source big data technologies like Spark, Iceberg, NiFi, etc

  • Experience with AI infrastructure and runtime technologies including NVIDIA GPUs, inference optimization, model serving, or distributed AI workloads

This role is not eligible for immigration support or relocation.

What you can expect from us:

  • Generous PTO Policy 

  • Support work life balance with Unplugged Days

  • Flexible WFH Policy 

  • Mental & Physical Wellness programs 

  • Phone and Internet Reimbursement program 

  • Access to Continued Career Development 

  • Comprehensive Benefits and Competitive Packages 

  • Paid Volunteer Time

  • Employee Resource Groups

EEO/VEVRAA

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How we rate this

Forward Deployed AI Engineer (Senior/Principal Level) at Cloudera rates 88 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGAI agentsFine-tuningML Ops

Also common in AI engineer jobs at this level

Pay

Typical AI engineer job
$182k

This posting doesn't list pay. Use the typical AI engineer figure when pay comes up. See AI engineer jobs

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How do you decide when an AI agent can act on its own versus asking for approval first?
  3. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  4. How do you monitor a model once it's live, and how do you know it needs retraining?
  5. How would you decide a model or AI system is ready to ship?

Adapt your resume

  • List these exact terms on your resume: RAG, AI agents, Fine-tuning, and ML Ops. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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