Level

Cloudera

Forward Deployed AI Engineer (Senior/Principal)

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

AI in this role

Build full-stack AI applications and agentic systems to accelerate AI adoption among strategic enterprise customers.

pythonpytorchlangchain
ragai-agentsfine-tuningml-opsgenerative-aiagentic-systemsfull-stack-developmententerprise-deployment

Business Area:

Corp. Strategy

Seniority Level:

Mid-Senior level

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 org, and is a specialized AI 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 hands-on AI engineer who can embed with customers, shape technical direction, and ship elegant, scalable, enterprise-quality AI applications. As a Forward Deployed AI Engineer you will:

  • Build & Prototype AI Apps - Develop full-stack AI 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 Strategy - Advise customer leaders on AI roadmaps, use case prioritization, deployment considerations, and align AI initiatives with business goals

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

  • Multiply impact - 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 2-3 years experience building Generative AI or agentic applications

  • Strong hands-on software engineering, data engineering, and applied AI/ML skills, with experience building full-stack AI 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

  • 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

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

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How we rate this

Forward Deployed AI Engineer (Senior/Principal) at Cloudera rates 85 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

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RAGAI agentsFine-tuningML OpsGenerative AIAgentic SystemsFull Stack DevelopmentEnterprise Deployment

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. Tell me about a project where generative ai was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: RAG, AI agents, Fine-tuning, ML Ops, and Generative AI. 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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