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We are looking for a Senior AI Engineer to design and build production-grade, LLM-powered systems. You'll work at the intersection of software engineering and applied AI - shipping agents, RAG pipelines, and tool-using systems that solve real problems at scale. This is a hands-on, high-ownership role for someone who thrives at the frontier of what's possible with modern LLMs and isn't afraid to write the glue, the infrastructure, and the prompts that make it all work.
This is a **cross-functional, company-wide role**. You won't be embedded in a single product team - instead, you'll partner with every department to identify high-leverage opportunities and build AI-powered tools and workflows that boost productivity and efficiency across the entire organization.
This is a great opportunity to be part of one of the fastest-growing infrastructure companies in history, an organization that is in the center of the hurricane being created by the revolution in artificial intelligence.
What You'll Do:
- Design, build, and operate LLM-powered applications, agents, and workflows end-to-end - from prototype to production.
- Architect retrieval, context engineering, and tool-use strategies that make models reliable, accurate, and cost-efficient.
- Integrate LLMs with internal services, third-party APIs, and data stores to automate complex business and engineering workflows.
- Build, evaluate, and continuously improve evaluation harnesses for non-deterministic systems.
- Collaborate closely with product, research, and platform teams to translate ambiguous problems into shipped capabilities.
- Stay ahead of the rapidly evolving LLM ecosystem (models, frameworks, agentic patterns) and bring the best ideas into our stack.

We are looking for a proactive and skilled Software Infrastructure Developer with a strong Agentic AI focus to join our team. In this role, you will design and build the infrastructure, tooling, and AI-powered automation that keep our engineering systems reliable, scalable, and intelligent.
You will be responsible for embedding GenAI and agentic capabilities into our internal developer platforms and CI/CD workflows - creating frameworks, libraries, and multi-agent systems that accelerate automation, improve system stability, and unlock new levels of engineering productivity. This is a hands-on role for someone who thrives at the intersection of infrastructure engineering and applied AI.
The ideal candidate brings deep technical expertise in Python (FastAPI) and/or TypeScript, Kubernetes, CI/CD (Jenkins, GitHub Actions), containerization, and cloud infrastructure - combined with hands-on experience building LLM-powered applications, agents, MCP servers, and RAG pipelines.
What you'll be doing:
AI Platform Development: Design and build internal AI platforms, reusable SDKs, libraries, tools, and MCP servers for agentic applications.
Agentic Workflow Development: Develop single- and multi-agent workflows covering planning, tool selection, delegation, state and memory management, execution, retries, fallbacks, error recovery, and human-in-the-loop approval.
AI-Powered Infrastructure & CI/CD: Integrate AI capabilities into Kubernetes infrastructure and Jenkins/GitHub Actions pipelines to improve development, testing, deployment, and operational workflows.
AI Observability & Evaluation: Implement agent tracing, prompt and tool-call logging, latency and error monitoring, token and cost tracking, regression testing, and failure analysis.
AI Security & Governance: Apply guardrails, access controls, auditability, and governance practices to ensure agents operate safely and reliably.
End-to-End Platform Ownership: Own AI platform capabilities from architecture through production and build reusable solutions that accelerate adoption across engineering teams.
Cross-Functional Collaboration: Collaborate with engineering and business teams to identify high-impact AI opportunities and translate them into scalable technical solutions.
AI Research & Innovation: Evaluate emerging AI frameworks, models, and cloud technologies and promote their adoption where they provide measurable value.

We are looking for a Senior Data Engineer to build and scale the data foundation behind platform and products. You will own complex data end-to-end - from ingestion and transformation through modeling, quality, observability, and production delivery.
This is a hands-on senior IC role for a strong builder who can solve difficult data problems independently, set a high technical bar, and collaborate closely with engineering, DS, and product. You will turn large, fragmented datasets into reliable, reusable capabilities that power every product.
Responsibilities
Build and own scalable data pipelines- Design, implement, and operate robust pipelines for high-volume structured and unstructured data, with validation, monitoring, lineage, and recovery built in.
Scale the platform for growth- A key near-term initiative is re-architecting the system to support a significantly larger customer base. You will own performance and cost-efficiency across pipelines and services, keeping reliability and operating costs under control as the platform scales.
Build across the stack- This is not a pipelines-only role. You will also write backend services and some frontend, including the internal backoffice the team runs on. We hire builders, not narrow specialists.
Own the core data tables- Own schema design and evolution, data contracts, and the modeling standards the team follows - naming, shared dimensions, normalization, documentation. Be accountable when a table is wrong, late, or drifting.
Level up the teams data work- Pair with and advise software engineers and data scientists on Spark, SQL, and modeling, and help turn notebook-grade code into production-grade pipelines.
Partner cross-functionally- Translate product, client, compliance, and business requirements into clear technical designs and dependable production systems.

We are looking for a Senior Backend AI Developer to architect and build the intelligent backbone of our platform: the multi-agent investigation pipeline, the data layer that consolidates signals from cloud providers and security tools, and the orchestration layer that ties it all together. You will work at the intersection of backend engineering and applied AI, designing systems that are durable, scalable, and smart enough to reason about complex cloud security environments.
What You'll Do:
Design and build the backend services that power Copperhelm's AI-driven investigation and remediation engine.
Architect and evolve a multi-agent system where specialized AI agents collaborate to investigate, prioritize, and resolve cloud security findings autonomously.
Build and optimize agent workflows using the Claude Agent SDK and Temporal.io for durable, orchestrated execution of long-running investigations.
Design and maintain the LLM inference pipeline, including prompt engineering, output validation, and integration with Amazon Bedrock.
Collaborate with product and engineering to translate threat models and investigation playbooks into automated agent workflows.
Make critical architectural decisions on agent boundaries, coordination patterns, data models, and scaling strategies.

We are looking for a detail-oriented and technically skilled Senior QA Engineer to join our engineering team and help ensure the quality, reliability, and security of our product.
In this role, you will own the quality of complex cybersecurity features from design through production, working closely with developers, product managers, and automation engineers. You will play a key role in defining test strategies, identifying risks early, and ensuring high-quality releases.
As part of your daily workflow, you will leverage AI-powered tools to improve test planning, discover edge cases, investigate defects, analyze logs, and enhance overall testing efficiency.
The Responsibilities:
Own the automation domain end-to-end: strategy, architecture, implementation, and continuous improvement
Design, build, and maintain scalable test automation frameworks across UI, API, integration, and system layers
Develop hands-on automation code in Python and/or TypeScript
Drive quality standards across engineering teams, partnering closely with developers, QA, DevOps, and product stakeholders
Define and implement automation best practices for reliability, scalability, maintainability, and fast feedback
Integrate and optimize automation within CI/CD pipelines to enable rapid, confident releases
Test complex SaaS, cloud, and cyber-related product flows, including security-sensitive scenarios, permissions, integrations, and system behavior
Use AI tools to support automation development, test generation, debugging, log analysis, documentation, and coverage improvement
Proactively identify coverage gaps and improve system testability, observability, and debuggability
Participate in system design and feature planning to embed quality from the earliest stages
Build and evolve testing environments using scripting, infrastructure-as-code, and cloud-native tools
Mentor and guide engineers, elevating automation standards across the organization

We are seeking a Software Engineer to join Dashs core development team. You will design, build, and ship the platform that powers discovery, session-aware detection, policy governance, and runtime enforcement across workstations, SaaS, and cloud. You will work on scalable, low-latency services that process agent activity in real time and enforce security policies without slowing builders down. This role requires strong engineering fundamentals, a focus on system reliability, and an interest in the unique challenges of agentic security.
Responsibilities:
Design and Implement Services: Design, develop, and deploy scalable backend services and APIs for the Dash Security platform (customer and admin APIs, detection ingestion, aggregation, and control-plane services).
Build Core Features: Implement features for agent/MCP/skill discovery, session and intent-aware detection, policies, and runtime enforcement across coding agents and enterprise AI platforms.
Optimize Runtime Protection: Maintain and optimize high-throughput detection and enforcement pipelines that operate at enterprise scale (ingestion, detectors, verdicts, and policy dispatch).
Integrate with Platforms: Build and deepen integrations with coding agents, SaaS AI platforms, identity providers, EDR, SIEM, and outbound action systems to ingest telemetry and enforce controls.
Ensure Reliability: Focus on code quality, performance, monitoring, and stability through robust testing, CI/CD, and operational excellence.
Collaborate with Research: Partner with the AI Security Research team to turn findings, signatures, and heuristics into production-ready detectors and enforcement logic.
Contribute to Architecture: Participate in technical design and architectural reviews across our codebases, agents, and event-driven backend services.
Stay Current: Continuously learn and apply new technologies, development methodologies, and security best practices.

If you're ready for the next big step in your software development career, read on.
You'll work across the frontend and backend systems that form the backbone of the platform: responsive, scalable interfaces on one side, and reliable backend services and APIs on the other. Our stack is Python, React, TypeScript, FastAPI, and several more database types running on Kubernetes, with an emphasis on performant, secure, and reliable systems.
You'll work directly with product, design, and the rest of engineering, on a team small enough that you'll shape the architecture and the roadmap, not just execute against them.
Responsibilities:
Develop and maintain the core architecture of our AI agent platform, across both frontend and backend
Design and deploy scalable APIs, microservices, and backend systems in Python (FastAPI, Flask, or Django)
Build high-quality, responsive UIs in React and TypeScript: reusable components, optimized for performance and accessibility
Build with coding agents as a primary mode of work, and help define how our team does it well, including tooling, review standards, and the guardrails that keep quality high
Own features end to end, from architecture through production deployment. Yes, this means brand new pillars in the platform
Maintain high code quality through testing, code review, and clear standards, which matters even more given how much code we generate

we are looking for a Senior Full Stack Engineer with a backend-oriented mindset to join our growing R&D team.
In this role, youll be responsible for connecting high-scale data infrastructure with our customer-facing portal-turning complex, runtime telemetry into fast, intuitive, and intelligent user experiences. Youll work closely with product, design, and Data pipeline and security engineers to build data pipelines, APIs, and integrations that make platform both powerful and delightful to use.
This is a hands-on role at the intersection of backend systems, high scale data engineering, and user experience, shaping how massive volumes of runtime data flow through data-driven platform.
Youll Be Great For This Role If You Love To:
Design and implement efficient data pipelines and backend integrations that connect our high-scale data to the user-facing portal.
Build and optimize APIs, data services, and caching layers to ensure fast and reliable data access.
Use data modeling, denormalization, and performance optimization to handle complex queries and large datasets effectively.
Collaborate closely with frontend engineers, product managers, and designers to ensure data flows seamlessly into intuitive UI components.
Contribute to the architecture and scalability of hybrid cloud-native platform.
Write clean, testable, production-ready code in Python, Node.js, TypeScript, and React, while keeping performance and maintainability top of mind.
Stay close to users and product feedback loops - helping translate technical insights into better experiences.
Own end-to-end delivery, from backend design through deployment using CI/CD, Temporal, and Kubernetes.

We are a well-funded, early-stage startup looking for a talented and motivated Backend Engineer specializing in infrastructure to join our founding team. The focus of this role is to build and scale the infrastructure that powers autonomous AI agents automating complex enterprise workflows. You will own the systems, pipelines, and platforms that let our AI agents run reliably, securely, and at scale in production.

Your Impact
Infrastructure & Platform

Design, build, and own the core infrastructure powering our AI agent platform, from data pipelines to production deployment systems.

Build and scale the backend systems that support high-throughput document processing and data extraction workloads.

Cloud Infrastructure and Scalability

Architect and deploy infrastructure on cloud platforms (AWS, GCP, or Azure) with a focus on scalability, reliability, and cost efficiency.

Own containerization and orchestration (Docker, Kubernetes) for all production workloads.

Build and maintain CI/CD pipelines and DevOps practices that let the team ship fast without breaking things.

Data Infrastructure

Design and manage data pipelines to process and analyze large volumes of documents and unstructured data at scale.

Build the infrastructure layer connecting AI agents to databases, vector stores, and enterprise systems (ERP, CRM).

API & Systems Integration

Build and maintain robust, well-documented APIs connecting AI agents with external systems and enterprise software.

Design for reliability: retries, observability, and graceful degradation across distributed systems.

Security and Compliance

Implement authentication and authorization mechanisms (OAuth2, JWT) to secure AI-driven systems.

Ensure compliance with data privacy standards (e.g. GDPR, HIPAA) and drive best practices for secure data handling across the infrastructure.

Monitoring and Optimization

Build observability and monitoring systems to track infrastructure health, performance, and cost.

Continuously optimize system performance for speed, reliability, and cost-efficiency at scale.

Collaboration

Work closely with AI/ML engineers, product, and the founding team to make sure infrastructure decisions support fast iteration and production-grade reliability.

Participate in code reviews, design discussions, and architecture planning to drive infrastructure strategy.

We're forming a new AI Group and looking for a Senior AI Engineer to help shape it from an early stage - a greenfield, long-term effort to evolve how decisions are made across the platform using AI-driven systems. You won't just integrate APIs or build demos; you'll build the AI brain that works alongside (and increasingly drives) our core automation engine, with real production impact from day one and room to grow into technical leadership as the group scales.

Agentic AI Architecture: Design and build autonomous AI agents that analyze infrastructure in real time and make intelligent decisions. Work with modern agentic frameworks (LangGraph, PydanticAI) and conversational AI to create multi-agent systems - including troubleshooting, optimization, FinOps, and how-to agents. Leverage core LLM capabilities (tool-use, memory, retrieval) to operate safely in production.
Platform Integration & Intelligent Decision Systems: Develop MCPs to expose capabilities to AI agents that reason over infrastructure environments, metrics, configurations, and cost signals. Build integrations with tools like Slack, Jira, and AI-powered IDEs (Cursor, Windsurf) to deliver context-aware insights, from "why is this pod not scheduling?" to "how can we reduce costs by 30% safely?"
AI Model Development & MLOps: Build and deploy machine learning models that learn from infrastructure patterns - detecting the right resource policies for workloads, predicting optimal scaling triggers, and recommending GPU configurations. Own the complete ML pipeline from training to production, ensuring models are reliable, monitored, and continuously improving.
R&D AI Tools Development & Adoption: Build and embed internal AI tools to accelerate engineering, development, research, and support.
AI Tools for Business Impact: Develop AI-powered tools that help Sales and Support teams demonstrate value instantly - agents that analyze customer infrastructure, generate cost optimization reports automatically, and turn technical data into clear business recommendations.
End-to-End Ownership: Own AI systems from concept to production, ensuring they're fast (sub-2-second responses), reliable, safe, and cost-effective. Build evaluation frameworks to measure quality, implement security controls, and balance performance tradeoffs in production.
Technical Leadership: Define AI architecture and best practices as a founding member of the AI team. Make key technical decisions - choosing frameworks, designing multi-agent systems, establishing data governance - and shape how evolves from AI-enhanced internal tools to customer-facing AI products.