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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Platform Engineer - Cloud Infrastructure - **Company:** Salesforce Inc. - **Location:** San Francisco, CA, United States - **Experience:** Expert - **Salary:** $148,500.0 - $223,900.0 - **Contract:** Permanent contract - **Skills:** Adobe InDesign, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Computer Programming, Continuous Delivery, Software Design Patterns, DevOps, Programming Tools, Python (Programming Language), Machine Learning, Octopus Deploy, Open Source Technology, Search Technologies, Software Engineering, Pulumi, Google Cloud, Cloud Platform System, GitHub Copilot, Large Language Models, Multi-Agent Systems, Prompt Engineering, Mttr, Multi-Cloud, Reliability of Systems, Generative AI, Core Api, Backend, Kubernetes, Infrastructure Automation Frameworks, Terraform, Oracle Cloud Infrastructure, Golang, Microservices - **Published:** July 17, 2026 - **Apply:** https://www.dice.com/job-detail/c68ad698-a85f-4f3e-b8ed-342a1b9e62cb ## About the Role 5+ years of professional experience in software engineering, platform engineering, or DevOps, with a recent, heavy focus on building and implementing AI solutions. Strong understanding of core AI and ML concepts applied practically to software engineering, including LLM context window optimization, embedding models, semantic search, vector databases, and prompt engineering/tuning. Experience building with agentic frameworks and LLM orchestration tooling to execute multi-step, autonomous tasks. Good programming skills in Golang and Python, with the ability to build production-grade backend services, APIs, and microservices. Solid fundamental knowledge of cloud-native infrastructure, with hands-on experience in Kubernetes and multi-cloud environments (AWS, Azure, Google Cloud Platform, or OCI). Familiarity with continuous deployment and infrastructure-as-code concepts (GitOps with Flux/Argo CD, Pulumi, or Terraform). Demonstrated agentic and automation mindset - you have a proven track record of using AI to automate complex workflows and can speak deeply on how you design AI systems to handle edge cases, tool-calling errors, and non-deterministic outputs. Strong communication and collaboration skills, with a passion for teaching, raising the team's AI literacy, and evangelizing AI solutions across engineering boundaries. Even Better If... Hands-on experience building custom extensions, plugins, or Model Context Protocol (MCP) servers for agentic developer tools like Claude Code or GitHub Copilot. Experience applying AI specifically to observability data (parsing logs, analyzing metrics, or correlating distributed traces) for predictive scaling or automated alerting. Deep experience working with vector databases (e.g., Pinecone, Qdrant, Milvus, pgvector) inside platform applications. Experience operating AI-driven tools within compliance-driven environments (FedRAMP, SOC 2), ensuring strong data privacy boundaries, LLM guardrails, and secure handling of sensitive cloud credentials. Experience with internal developer platforms (IDPs), platform APIs, or building developer experience (DevEx) tooling. Contributions to open-source projects is a plus ## Description We are looking for a Senior Member of Technical Staff with strong AI/ML software engineering expertise to build the next generation of intelligent, self-healing platform tools. Instead of managing GPU hardware, your focus will be applying AI solutions directly to infrastructure and operations problems. You will write core platform services in Go and Python, design multi-agent workflows to automate complex operational tasks, build RAG systems over engineering documentation, and act as the core AI amplifier - architecting the intelligent systems that multiply the entire engineering organization's output. What You'll Actually Be Doing Success will be measured by how effectively you integrate AI, LLMs, and autonomous agents into our multi-cloud platform services to improve system reliability, reduce operational toil, and elevate the developer experience. Design, build, and operate platform services and infrastructure automation in Go and Python, embedding AI capabilities directly into the core platform software. Architect and implement intelligent, closed-loop automation systems (AIOps) that leverage LLMs and autonomous agents to detect anomalies, perform root-cause analysis, and execute self-healing remediation playbooks. Build and maintain Retrieval-Augmented Generation (RAG) applications over internal platform documentation, runbooks, and historical incident data to drastically reduce engineering MTTR. Develop custom tools, CLI plugins, and Model Context Protocol (MCP) integrations that connect our cloud infrastructure APIs to agentic coding tools (like Claude Code), turning standard automation into autonomous workflows. Partner with SRE, security, and platform specialists to identify highly repetitive operational work and build agentic solutions that delegate that toil to AI. Maintain and improve standard continuous deployment pipelines using GitOps tooling (Flux, Argo CD) and infrastructure-as-code frameworks (Pulumi, Terraform) to ensure safe, repeatable delivery of both traditional platform code and AI-driven solutions. Participate in design reviews, write clear technical documentation and RFCs, and mentor traditional platform engineers on AI/ML concepts, prompt engineering, and agentic design patterns. Contribute to on-call rotations and continuously bring an AI-first perspective to improving incident management and platform post-mortems. ## Related Videos - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [What Developers Get Wrong About Application Quality](https://www.wearedevelopers.com/videos/233-what-developers-get-wrong-about-application-quality) - [Go with the Flow: Stop the Leaks Before Your Memory's a Waterfall!](https://www.wearedevelopers.com/videos/100073-go-with-the-flow-stop-the-leaks-before-your-memory-s-a-waterfall) - [#90DaysOfDevOps - The DevOps Learning Journey](https://www.wearedevelopers.com/videos/548-90daysofdevops-the-devops-learning-journey) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) ## Related Articles - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Got AI ideas but no money? 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