Forward Deployed Engineer - Architect

About the role

Forward Deployed Engineer - Architect1

What you will do

• Lead discovery with business users and technology teams. Map current workflows, identify bottlenecks, assess data and system readiness, and agree on a focused problem and measurable success criteria.
• Translate customer priorities into a practical solution design and delivery plan. Make informed choices about where AI adds value, where conventional software is sufficient, and where human review is needed.
• Build production-quality AI applications, agents, and supporting services. Develop APIs, retrieval pipelines, tool integrations, orchestration logic, and user interfaces where required.
• Connect solutions to enterprise applications and data sources. Address authentication, permissions, data quality, and reliable execution of actions across systems.
• Establish evaluation datasets and acceptance criteria. Test task completion, output quality, failure modes, latency, and cost; use findings to improve the solution before and after deployment.
• Own production readiness with customer and Jade teams. Implement access controls, auditability, monitoring, safe fallbacks, human approvals, and operational handover; troubleshoot issues after launch.
• Provide technical direction to the delivery team while writing and reviewing code. Manage dependencies, surface risks early, and explain scope, architecture, and delivery tradeoffs clearly to customers.
• Measure adoption and business impact against the agreed baseline. Turn successful patterns into reusable components and contribute field feedback to Jade Orbit and Jade's FDE delivery methods.

What you bring

• 6+ years of hands-on software engineering experience, including 2+ years building LLM-powered applications or agentic systems. Demonstrated ownership of multiple production deployments, with the ability to explain the code you wrote, architecture decisions, failure modes, and results.
• Strong Python engineering skills, including asynchronous programming, API development, automated testing, and debugging. Solid grounding in SQL, data structures, distributed systems, and application design; ability to contribute across backend and user-facing components.
• Deep understanding of LLM application architecture, including model selection, embeddings, context management, structured outputs, tool calling, and inference tradeoffs. Ability to balance model quality, latency, cost, and privacy requirements.
• Hands-on experience engineering agents and orchestration using a framework or custom implementation. Ability to implement explicit state, persisted checkpoints, retries, idempotent tool execution, human approvals, and recovery from partial failures.
• Ability to build and tune retrieval-augmented generation pipelines, including document ingestion, chunking, embeddings, hybrid search, metadata filtering, reranking, and access-aware retrieval. Experience diagnosing retrieval quality and grounding failures.
• Experience developing rigorous AI evaluations using representative datasets, task-level metrics, regression tests, and adversarial cases. Ability to measure factual accuracy, tool-use correctness, task completion, and failure rates rather than relying on subjective demonstrations.
• Strong enterprise integration skills across APIs, authentication, databases, and event-driven systems. Ability to expose business capabilities as secure tools and implement permission boundaries for agents that read data or execute actions.
• Hands-on production deployment and operations experience on a major cloud platform, including containers, CI/CD, secrets management, tracing, monitoring, and incident diagnosis. Ability to control concurrency, rate limits, inference costs, and operational reliability.
• Practical experience securing AI applications against prompt injection, sensitive-data exposure, and unauthorized tool execution. Ability to implement least-privilege access, audit trails, validation, and human review where consequences require it.
• Ability to lead technical discovery and architecture discussions directly with customers, turn incomplete requirements into working software, and guide distributed engineers while continuing to write and review production code.

Experience that would be valuable

• Previous work as an FDE, customer engineer, hands-on technical consultant, technical founder, or solutions architect with substantial coding and deployment ownership.
• Experience with agent orchestration frameworks, Model Context Protocol integrations, LLM observability, evaluation pipelines, or model selection and fine-tuning when justified by the use case.
• Experience building enterprise AI foundations, modernizing applications with AI, or working with open-source models and private deployment requirements.
• Delivery experience in manufacturing, high-tech, or healthcare and life sciences, and collaboration with distributed engineering teams.

How success will be measured

• Time to a useful first release and progression to reliable production use.
• Adoption and measurable improvements in customer workflows, such as cycle time, quality, productivity, or cost.
• Solution reliability, evaluation results, and effective management of operating costs.
• Customer trust, clear technical ownership, and reusable engineering assets that improve future delivery.