Sr. AI Engineer (Data Science Team)
Cloudera is hiring a Sr. AI Engineer (Data Science Team) for a remote role open to applicants in Costa Rica. Level rates it ; you can apply on Level.
Level's read on this job
- Builds AI ●●●● is how Level rates this AI engineer role: the job is building AI systems.
- The posting lists this role as remote.
- Tools and skills the posting cites include scikit-learn, prompt engineering and RAG.
- Level files it as a senior role.
- Level lists 57 other open roles at Cloudera.
AI in this role
Business Area:
ITSeniority Level:
Mid-Senior levelJob 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.
At Cloudera, we believe that data can make what is impossible today, possible tomorrow. We empower organizations to transform complex data into clear and actionable outcomes. Join us in our mission to harness the power of data.
We are seeking a talented and curious Senior AI Engineer to join our fast-paced, data-driven data science team. In this role, you will design and deliver AI-powered systems and applications that accelerate decision-making and enhance operational excellence.
You will combine strong statistical foundations, advanced programming expertise, and modern Generative AI techniques to build scalable, production-ready solutions. This is a builder-focused role. You will move beyond analysis to develop internal copilots, AI-enabled workflows, and reusable platform components that embed intelligence directly into business processes.
Our work empowers leadership and operational teams by creating measurable, AI-enabled capabilities. We seek a thoughtful and pragmatic innovator who is enthusiastic about GenAI, disciplined experimentation, and building durable internal AI infrastructure.
To succeed in this role, you will demonstrate technical depth, intellectual curiosity, and a strong builder mindset:
- Generative AI & LLM Engineering: Hands-on experience working with large language models (LLMs) and modern AI tooling. This includes prompt design, structured output generation, retrieval-augmented generation (RAG), evaluation strategies, and workflow automation. Ability to translate GenAI capabilities into reliable, enterprise-ready solutions that integrate with existing systems and data sources.
- AI Application Development Experience: rapidly prototyping and iterating on internal applications, copilots, or AI-enabled workflow tools. Comfortable evolving prototypes into maintainable, production-grade solutions. Familiarity with modern development frameworks (e.g., Streamlit, Gradio, FastAPI, or similar) is beneficial.
- Platform-Oriented Thinking: Demonstrated ability to design reusable components such as shared prompt libraries, retrieval pipelines, evaluation frameworks, and standardized integration patterns that enable scalable AI adoption.
- Data Science & Machine Learning Expertise: Proficiency in Python (or R) for data preparation, feature engineering, statistical modeling, and machine learning. Experience with core data science libraries (e.g., Pandas, NumPy, scikit-learn) and a solid understanding of supervised and unsupervised learning methods.
- Strong Mathematical and Statistical Foundation: Deep understanding of probability, statistical inference, experimentation, and quantitative reasoning to ensure model robustness and reliability.
- SQL & Data Fluency: Strong understanding of relational databases and the ability to quickly learn new schemas and data environments. Comfortable writing efficient, production-grade SQL to support modeling, experimentation, and AI-enabled applications.
- Exceptional Communication Skills: Ability to translate complex business challenges into technical solutions and clearly communicate findings, trade-offs, and recommendations to both technical and non-technical stakeholders.
- Collaborative Development Experience: Experience working in collaborative environments such as Cloudera Data Science Workbench, Jupyter, Zeppelin, or similar platforms.
- GitHub Proficiency: Experience using version control to support collaboration, code review, documentation, and long-term maintainability.
As a Sr. AI Engineer, you will:
You will apply rigorous analytical thinking and modern AI capabilities to design, build, and scale high-impact solutions.
- Design, develop, and deploy GenAI-powered internal applications, copilots, and workflow accelerators.
- Build reusable AI components, including retrieval pipelines, structured prompting patterns, orchestration workflows, and evaluation harnesses.
- Design retrieval strategies that connect LLMs to trusted internal knowledge sources, ensuring grounded and reliable outputs.
- Develop and maintain statistical and machine learning models to support automation, optimization, forecasting, and classification use cases.
- Implement evaluation and validation frameworks to measure quality, accuracy, and consistency of AI-driven systems.
- Partner cross-functionally to identify high-value opportunities for AI enablement across the organization.
- Create reusable datasets, feature pipelines, and experimentation frameworks to support iterative development.
- Uphold high standards for quality, reliability, and responsible AI practices.
- Contribute to peer review processes to ensure technical rigor and maintainability.
- Document methodologies, assumptions, and implementation details to ensure transparency and reproducibility.
We are excited if you have (Required Experience):
- Hands-on experience building applications or workflows powered by large language models (LLMs).
- Evidence of a builder mindset through shipped AI tools, internal platforms, or automation solutions.
- Demonstrated experience applying machine learning techniques in production or enterprise environments.
- 5+ years of relevant experience in Data Science, Machine Learning, or AI-focused roles.
- Strong curiosity for emerging AI technologies and the ability to evaluate and adopt them responsibly.
- Academic background in a quantitative discipline such as Statistics, Mathematics, Computer Science, Engineering, Economics, or a related field.
You may also have: (Preferred Qualifications)
- Experience with vector databases, embedding models, or semantic retrieval systems.
- Experience designing internal AI platforms or shared enablement frameworks.
- Familiarity with API-driven architectures and integrating AI capabilities into enterprise systems.
- Exposure to responsible AI practices, governance frameworks, or model lifecycle management.
This role is not eligible for immigration sponsorship.
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
Employee Resource Groups
EEO/VEVRAA
#LI-SZ1
#LI-Remote
How we rate this
Sr. AI Engineer (Data Science Team) at Cloudera rates 97 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.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ 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
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
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you think about the risk of an AI system in this kind of role failing silently?
- What's a project where you used scikit-learn hands-on?
- How would you decide a model or AI system is ready to ship?
Adapt your resume
- List these exact terms on your resume: Prompt engineering, RAG, AI Safety, and scikit-learn. 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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