Staff Full Stack Software Engineer, Platform Engineering

Cloudera

Atlanta (GA)

Hybrid

USD 180,000 - 260,000

Full time

14 days+

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Benefits offered by this job

Generous PTO Policy

Flexible WFH Policy

Access to Continued Career Development

Mental & Physical Wellness programs

Paid Volunteer Time

Job summary

A cloud technology company in Atlanta is seeking a Staff Full Stack Software Engineer to lead the architecture and delivery of AI-powered workflows. The role involves collaborating across functions to implement innovative solutions and ensuring high performance and security in a hybrid cloud environment. Candidates should have expertise in programming languages, experience with AI model integration, and a strong background in cloud-native architectures. A Bachelor's degree in Computer Science and over 6 years of related experience is required.

Qualifications

6+ years of experience in software engineering, preferably with AI integration.
Experience in designing reusable AI workflow primitives is a plus.
Familiarity with tools like MLflow, LangChain, or Hugging Face is advantageous.

Responsibilities

Lead the architecture and delivery of AI-powered workflows.
Build reliable, low-latency services that integrate AI models.
Define technical strategy and quality standards for delivery.

Skills

Expertise in at least one primary programming language (Rust preferred)

Experience with cloud-native architectures

Familiarity with AI/ML model integration

Security & privacy mindset

Mentoring and technical leadership

UI integration

Security mindset

Education

Bachelor’s degree in Computer Science or equivalent

Tools

Kubernetes

Python

Rust

Pinecone

Weaviate

pgvector

Job description

Staff Full Stack Software Engineer, Platform Engineering

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 largest enterprises.

Business Area: Engineering

Seniority Level: Mid-Senior level

Job Description:

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.

As a Staff Full Stack Software Engineer you will:

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.

We are excited about you if you have:

Bachelor’s degree in Computer Science or equivalent, and 6+ years of experience.
Expertise in at least one primary language (Rust preferred) and ecosystem (e.g., Python, 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.

You might also have:

Platform thinking: Experience designing reusable AI workflow primitives, SDKs, or internal platforms used by multiple product teams.
Model ops: Experience with model lifecycle management, feature/embedding stores, prompt/version management, and offline/online eval systems.
Search & data infra: Experience with vector databases (e.g., Pinecone, Weaviate, pgvector), retrieval strategies, and indexing pipelines.
Observability: Built robust tracing/metrics/logging for AI systems; familiarity with quality dashboards and prompt diff tooling.
Cost strategy: Experience with model selection, distillation, caching layers, router policies, and autoscaling to manage spend.
Experience with managing machine learning workloads on container orchestration platforms like Kubernetes, including setting up GPU resources, managing distributed training jobs, and deploying models at scale.

Why this role matters:

This is more than cloud management, it’s about building the foundation for a consistent, secure, and compliant cloud experience that gives organizations 100% access to 100% of their data, anywhere.

With the recent acquisition of Taikun, we are simplifying Kubernetes and cloud management even further, creating a platform that is unified, scalable, and future-ready.

If you are passionate about Kubernetes, not just using it but building it at the core managing workloads across hybrid clouds and datacenters and obsessed with performance, devops, etc. this is where you belong.

This role is not eligible for immigration sponsorship

What you can expect from us: