About the role
Business Area: Professional Services 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. As an AI Solutions Engineer within Cloudera’s Public Sector Consulting team, you will be the technical architect and execution lead for agencies moving from "data chaos" to "agentic autonomy." You will work directly with government organizations to design, build, and deploy mission-critical AI applications on the Cloudera Data Platform (CDP). This is not a "theoretical" role. You will be on the front lines of Phase 2 and Phase 3 adoption journeys—helping customers clean legacy data silos, select the right model architectures, and industrialize MLOps pipelines in highly secure, often air-gapped or hybrid-cloud environments. As the AI Solutions Engineer you will: 1. AI Model Strategy, Selection and Implementation Evaluate and select optimal model architectures (LLMs, SLMs, or traditional ML) based on mission
requirements, considering tradeoffs between accuracy, latency, and cost. Guide customers on "Build vs. Buy vs. Fine-tune" decisions, prioritizing open-source models (Llama, Mistral, Falcon) that can run securely within a sovereign data perimeter. Experience building Agentic Workflows (AI agents that can execute API calls and multi-step tasks). 2. End-to-End Data Engineering Design and implement robust data pipelines within CDP to transform "messy" legacy data into AI-ready formats. Develop and optimize Vector Databases and Retrieval-Augmented Generation (RAG) architectures to ground AI responses in verified agency facts. Build Data pipelines with Spark, Nifi, Kafka or other ETL tools. 3. Optimization & Performance Tuning Optimize model inference for production environments using quantization, pruning, and hardware acceleration (NVIDIA GPU orchestration). Implement LLMOps to monitor model performance, detect hallucination rates, and manage model versioning and drift. 4. Public Sector Advisory & Governance Collaborate with the customer’s AI Center of Excellence (CoE) to establish automated guardrails for ethics, bias mitigation, and FedRAMP/IL5 compliance. Translate complex technical AI concepts into mission-value briefings for GS-level stakeholders and agency leadership. We’re excited about you if you have: (Minimum
