You will be responsible for shaping the technical architecture of AI-powered experiences across the Anaplan platform. This includes designing intelligent agent architectures capable of understanding business intent and context, determining the appropriate actions or information required, interacting with enterprise systems and executing workflows in a secure and controlled manner.
You will help evolve Anaplan from traditional conversational experiences toward sophisticated agentic systems that can reason, plan, retrieve information, use enterprise tools and collaborate across multiple services. You will establish architectural patterns for agent orchestration, state and memory management, tool and function calling, context management, retrieval, workflow execution and human-in-the-loop controls.
A significant part of the role will involve designing Conversational AI architecture that allows users to interact naturally with complex planning and decision-making capabilities. You will think deeply about multi-turn conversations, context preservation, intent understanding, response generation, grounding, explainability and the boundary between deterministic business logic and probabilistic AI.
You will also architect Retrieval-Augmented Generation solutions that enable AI systems to work effectively with enterprise knowledge and data. This will require strong understanding of embeddings, semantic and hybrid retrieval, vector search, metadata, ranking, context construction, authorization-aware retrieval and techniques for improving grounding and reducing hallucinations.
You will evaluate and apply modern foundation models and AI technologies based on business requirements rather than simply adopting the latest technology. You will consider model capability, reasoning quality, latency, cost, context windows, reliability, structured outputs, tool calling and model routing, and determine where approaches such as prompting, RAG, fine-tuning, conventional software or agentic workflows are most appropriate.
Although the role is architecturally oriented, you will remain technically close to the implementation. You may write code in Python, Java, C#, Go or other relevant technologies to build prototypes, validate architecture, develop AI workflows, implement critical components or demonstrate technical feasibility.
You will be expected to understand implementation details deeply enough to challenge designs, identify technical risks and make informed architectural decisions. The strongest candidate will be someone who can move naturally between designing a distributed AI platform, reviewing an architecture document, experimenting with an LLM-based workflow and writing the code required to prove that the approach works.
You will work closely with engineering teams to ensure that architecture does not remain theoretical. You will help translate architectural concepts into scalable, maintainable and production-ready software and will remain engaged through implementation, performance optimization and operationalization.
Enterprise AI Architecture
The systems you design will need to operate within the realities of enterprise software. You will therefore consider scalability, availability, resilience, observability, security, privacy, governance, performance and cost as first-class architectural concerns.
You will design AI capabilities that integrate with microservices, APIs, event-driven systems, enterprise data platforms and cloud infrastructure. You will work across service boundaries and help establish patterns for asynchronous processing, orchestration, caching, fault tolerance, high availability and graceful failure.
You will also help establish the architectural foundations required to make AI systems measurable and trustworthy. This includes approaches for evaluating response quality, groundedness, hallucination, retrieval performance, agent success, tool selection, latency, token consumption and overall task completion.
Security and Responsible AI
Enterprise AI introduces unique security and governance challenges, and you will be expected to incorporate these considerations directly into the architecture. You will work with security and platform teams to address concerns such as data privacy, identity and authorization, secure retrieval, prompt injection, data leakage, agent permissions, tool access, auditability and protection of sensitive enterprise information.
The architecture should enable AI systems to be powerful without becoming uncontrolled. You will help establish appropriate guardrails, authorization boundaries, monitoring and human-approval mechanisms for agent-driven actions.
At Engineer V level, you will operate as a senior technical authority and architectural leader. You will influence engineering teams through strong technical judgment, design thinking and hands-on credibility.
You will work closely with Product, Engineering, AI/ML, Data, Security, Platform and UX teams to translate complex business requirements into technically sound solutions. You will lead architecture discussions, drive important technical decisions, establish reusable design patterns and influence the long-term AI technology roadmap.
You will also mentor senior engineers and architects, raise engineering standards and help create a culture where AI solutions are designed with the same rigor expected of mission-critical enterprise software.