• Design for Action: Build AI integrations that do more than just summarize text. Design systems that can securely read from and write back to our core platforms (e.g., updating a record in Salesforce or drafting a response in Zendesk). • Secure Data Flow: Architect the integration layer between external LLM providers (Google, Anthropic, OpenAI) and internal data. Ensure all data retrieval is governed by strict permissions so users are only able to access data they are authorized to see. • Scalability: Design a model-agnostic inference layer that allows us to switch between models based on performance and cost requirements. • Backend Engineering: Write production-ready code using modern agent frameworks (ADK, LangChain). This is a technical role that requires coding. • Retrieval Augmented Generation (RAG): Implement robust RAG pipelines to ground AI responses in company data. This includes managing vector databases and optimizing search strategies (hybrid search, reranking) to ensure accuracy. • Deployment: Set up the CI/CD pipelines and infrastructure required to deploy and maintain these services in a cloud environment. • Evaluation: Move beyond "eye-balling" results. Implement automated testing frameworks to measure response accuracy, latency, and costs before deploying changes. • Data Privacy: Ensure strict handling of PII and adherence to enterprise security standards. You will be the gatekeeper for how sensitive data is exposed to LLMs. • Requirements Gathering: Partner with leaders in Sales, Marketing, and Support to identify high-impact automation opportunities. Translate business needs into technical specifications.

• Work with enterprise customers and internal teams to turn business workflows into scalable, production-ready agentic AI systems. • Design and build LLM-powered agents that reason, plan, and act across tools and data sources with enterprise-grade reliability. • Balance rapid iteration with enterprise requirements, evolving prototypes into stable, reusable solutions. • Define and apply evaluation and quality standards to measure success, failures, and regressions. • Debug real-world agent behavior and systematically improve prompts, workflows, tools, and guardrails. • Contribute to shared frameworks and patterns that enable consistent delivery across customers.

• Define the long-term technical vision and architecture for AI-powered developer tools and the self-service platform that enables teams to build their own AI agents • Establish architectural patterns, technical standards, and best practices for LLM integration, AI agent development, and production AI systems serving developers • Lead technical strategy for platform capabilities including deployment frameworks (ArgoCD/GitOps), observability integration (Grafana), security controls, and operational tooling for AI developer tools • Design highly available, scalable infrastructure for hosting AI agents and developer tools with predictable performance and intelligent resource management • Drive technical decisions on AI technology choices, LLM provider strategies, prompt engineering approaches, and agent orchestration frameworks • Partner with Senior Manager and product leadership to align technical architecture with business objectives and productization opportunities • Architect and build production-ready AI agents for developer productivity including code review assistants, test generators, deployment diagnostics, and incident response automation • Design and implement the self-service platform infrastructure that reduces time-to-production for new AI tools from weeks to days • Build systems that accelerate adoption of AI-native development tools (Claude Code, Cursor, Warp) across Docker's engineering organization • Evaluate emerging AI/LLM technologies, developer tooling trends, and agent frameworks to inform Docker's technical strategy • Drive technical standards for measuring AI tool effectiveness including adoption metrics, productivity gains, and developer satisfaction.

• Participate in presales discussions with potential clients • Design AI architectures, including LLM-based solutions and AI Agents • Provide technical estimations for AI projects • Develop quick Proof-of-Concepts (PoCs) to showcase AI capabilities • Build simple UI demos for AI solutions (React, Next.js) • Work closely with ML engineers, software architects, and frontend developers to onboard them into new projects • Engage with stakeholders to understand business needs and AI opportunities • Provide technical mentorship to AI engineers on best practices • Collaborate with BA, CTO, and sales managers during presales