Staff Software Engineer - Agentic AI Platform

Cloudera•Austin, TX

3d•Remote

About The Position

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. Our Data Services Pillar is the heart of data innovation. We don’t just work with technology; we build it. Our mission is to empower data practitioners by creating seamless, enterprise-grade experiences for data engineering, warehousing, streaming, operational databases, and AI. Join Cloudera’s Machine Learning Platform team as a Staff Software Engineer. You will be a core technical leader responsible for designing, building, and delivering our next-generation AI and MLOps platform. You will design, build, and deliver an enterprise-grade platform enabling enterprises to create, deploy, and orchestrate agentic applications and multi-agent platforms using foundation models with enterprise data at scale in a hybrid cloud environment. As a Staff-level engineer, you will drive technical architecture, advocate for engineering best practices, and collaborate closely with cross-functional teams (Product, Design, Frontend, and Field Engineering) to enhance developer velocity and platform agility.

Requirements

Experience: 8+ years of software engineering experience building scalable backend microservices and distributed systems.
Core Languages: Deep expertise in Python Go, Java, or C#/C++, along with gRPC and SQL.
Cloud & Containerization: Extensive hands-on experience designing and developing microservices on Kubernetes, plus expertise in at least one major cloud platform (AWS, GCP, or Microsoft Azure).
Agentic Frameworks & Evaluation: Proven experience with open-source agentic frameworks((e.g., LangGraph, AutoGen, CrewAI) and evaluation tooling (LangSmith/Langfuse or Phoenix).
Agentic & Generative AI Mastery: Proven experience building applications utilizing advanced LLM orchestration paradigms (e.g., ReAct loops, planning/reflection frameworks, multi-agent systems) alongside standard foundational models, prompt engineering, and RAG architectures using vector databases (e.g., Pinecone, Milvus).
Memory & State Infrastructure: Deep understanding of caching layers, relational databases, and vector databases optimized for agentic state persistence and memory retrieval.
System Design: Demonstrated ability to go deep into complex distributed systems, crafting both high-level architecture and low-level technical designs.
Education: BS/MS in Computer Science, Software Engineering, or a related field (or equivalent professional experience).
Soft Skills: Self-driven with a strong sense of ownership, paired with excellent written and verbal communication skills.

Nice To Haves

AI/ML Orchestration: Experience with ML orchestration and serving software ( KServe, Knative).
Big Data: Familiarity with distributed data technologies like Apache Spark, Hive, etc.
Full-Stack Exposure: Experience with React, HTML, and CSS to better collaborate with UI teams.
Data Science Ecosystem: Experience building applications alongside data scientists using tools like Python, TensorFlow, PyTorch, MLflow, or R.
Agile Environments: A proven track record of collaborating with agile teams across geographically dispersed and remote locations.

Responsibilities

Architect and Build: Design, code, and implement elegant, scalable, enterprise-quality platform and application services / SDKs that support autonomous agents, multi-agent collaboration, and advanced tool-use capabilities.
Implement Agentic Evaluation & Observability: Build automated evaluation pipelines and trajectory tracking to continuously bench, audit, and evaluate agent reasoning loops, tool-calling accuracy, and guardrail compliance.
Architect Memory Management Systems: Develop and optimize robust stateful memory architectures for enterprise agents, handling short-term context window strategies, long-term semantic/episodic memory persistence, and secure cross-session state management.
Enable RAG use cases: Implement AI application services powered by machine learning models, enabling scalable Retrieval-Augmented Generation (RAG) and semantic search.
Lead by Example: Advocate for and establish engineering best practices, coding standards, and rigorous system design methodologies.
Enhance Platform Velocity: Work to enhance developer velocity, framework abstraction, and team agility across the AI platform ecosystem.
Collaborate Cross-Functionally: Build strong relationships and collaborate with platform and UI engineers, data scientists, quality engineers, UX designers, Product Management, Field Engineering, and external enterprise partners.
Cross-Functional Collaboration: Build strong technical relationships with platform engineers, UI developers, UX designers, and Product Management to deliver cohesive user experiences.
Mentorship: Act as a senior technical leader on the team, mentoring junior engineers and actively contributing to a culture of engineering excellence and craftsmanship.