Staff Engineer, Cloudera

• Seniority Level: Mid-Senior level
• Bachelor’s degree in Computer Science or equivalent, and 6+ years of experience
• Expertise in at least one primary language (Rust preferred) and ecosystem (Go, or Java) and cloud-native architectures (containers, service mesh, queues, eventing)
• Proven experience in integrating AI/ML models into user interfaces. This is more than just calling an API; you should have experience building features like AI-powered assistants, natural language interfaces (e.g., text-to-SQL), proactive suggestions, or intelligent data visualization
• Familiarity with the AI/ML ecosystem: You understand the fundamentals of LLMs, vector databases, RAG, and prompt engineering. Familiarity with tools such as MLflow, LangChain, or Hugging Face is a significant advantage
• Security & privacy mindset: Familiarity with data governance, PII handling, tenant isolation, and compliance considerations

Desirable

• Ready to take cloud innovation to the next level? Join Cloudera’s Anywhere Cloud team and help deliver a true “build your own pipeline, bring your own engine” experience. enabling data and AI workloads to run anywhere, without friction or vendor lock-in
• We take the best of the public cloud- cost efficiency, scalability, elasticity, and agility and extend it to wherever data lives: public clouds, private data centers, and even the edge. Powered by Kubernetes, our hybrid architecture separates compute and storage, giving customers maximum flexibility and optimized infrastructure usage
• We are looking for a Staff Full Stack Software Engineer to lead the architecture and delivery of AI-powered workflows that are core to our product
• You will define the technical strategy, set quality and reliability standards, and deliver end-to-end systems that transform ambiguous customer needs into robust, measurable, and privacy-safe AI experiences
• You’ll partner closely with Product, Design, Data Science, and GTM to deliver high-impact features at scale
• Own the architecture: Design, evolve, and document the end-to-end AI workflow stack (prompting, retrieval, tools/function-calling, agents, orchestration, evaluation, observability, and safety) with clear interfaces, SLAs, and versioning
• Ship production systems: Build reliable, low-latency services that integrate foundation models (hosted and self-hosted), and traditional microservices
• Own end-to-end delivery of features from the user-facing aspect (UI) to the backend services
• Implement robust testing frameworks, including unit, regression, and end-to-end tests, to guarantee deterministic and predictable behavior from our AI-powered data platform. Establish safety guardrails and human-in-the-loop processes to maintain accuracy and ensure the production of ethical, responsible, and non-toxic outputs
• Optimize for cost & performance: Instrument, analyze, and optimize unit economics (token usage, caching, batching, distillation) and performance (p95 latency, throughput, autoscaling)
• Drive data excellence: Shape data contracts, feedback loops, labeling strategies, and feature stores to continuously improve model and workflow quality
• Mentor and multiply: Provide technical leadership across teams, unblock complex projects, raise code/design standards, and mentor senior engineers
• Partner across functions: Translate product intent into technical plans, influence roadmaps with data-driven insights, and communicate trade-offs to executives and stakeholders