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

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