Staff Applied AI Engineer, Agents

About The Role Morena is leading the search for a Staff Applied AI Engineer to build and deploy production AI agents for complex enterprise workflows. This is a senior, hands-on engineering role for someone who can take difficult AI deployment problems from initial design through production and turn what is learned from individual implementations into reusable engineering patterns. You will work on AI agents operating across communication, operational, and transaction-heavy workflows in a large regulated industry. These systems need to do substantially more than generate responses. They need to reason through multi-step processes, interact with tools and APIs, follow business rules, manage state, and operate reliably under real production constraints. This is not a traditional solutions engineering role. You will be expected to design, build, debug, evaluate, and ship production systems while working closely with customers, product engineers, and platform teams. What You'll Own Complex AI deployments Take technical ownership of sophisticated AI agent deployments from initial design through production Understand the underlying workflow, design the right agent architecture, integrate with customer systems, evaluate behavior, resolve edge cases, and ensure reliable production performance Own outcomes rather than simply completing one piece of the implementation Agent workflows Design and iterate on production AI systems involving agent orchestration, prompt and context design, tool use, structured workflows, external API integrations, state management, retrieval, business logic, human escalation, and failure recovery Apply strong engineering judgment about where probabilistic AI belongs and where deterministic application logic should take over Evaluation and agent quality Establish how agent quality is measured and improved using evaluation datasets, automated checks, model-based evaluation, regression tests, production monitoring, trace analysis, failure classification, and real-world outcome metrics Investigate why agents fail, identify the underlying cause, and improve the system rather than relying on repeated prompt adjustments Reusable engineering patterns Turn lessons from individual deployments into shared components, agent patterns, templates, internal tooling, integration approaches, evaluation methods, and deployment playbooks Make each difficult implementation faster and more reliable for future work Product and platform collaboration Work closely with Product and Platform engineering to bring lessons from production deployments back into the core product Distinguish between customer-specific requirements, reusable platform capabilities, product gaps, integration problems, model limitations, and workflow design problems Influence the technical roadmap with real production experience Production debugging Diagnose difficult agent behavior across models, prompts, context, integrations, tools, infrastructure, and customer systems Move comfortably between reading production traces, investigating failed tool calls, debugging APIs, reviewing prompt or context construction, analyzing evaluation results, tracking distributed-system failures, and writing production code Technical leadership Raise the engineering bar through technical reviews, architecture decisions, mentorship, and the quality of systems you personally build Provide guidance to other engineers while continuing to own significant production work What We're Looking For Software engineering experience 6+ years of professional software engineering experience with a strong record of building and operating production software Comfortable with Python, APIs and services, cloud infrastructure, databases, distributed systems, integrations, observability, testing, and production operations Production AI experience Hands-on experience building systems using modern LLMs, including agentic systems, tool-calling, prompt and context engineering, LLM workflows, retrieval, structured generation, model APIs, and agent frameworks Production experience is significantly more important than experimentation alone Strong engineering fundamentals Approach AI systems as production software with reliability, failure modes, testing, observability, data flow, API design, deployment, and operational risk in mind Identify whether issues come from the model, surrounding software, an integration, available context, workflow design, or evaluation method Agent quality and failure analysis Reason systematically about why an AI system behaves incorrectly Improve agents through changes to context, tools, workflow structure, prompts, models, evaluations, business logic, guardrails, or underlying integrations Use evidence rather than intuition alone to determine whether a change actually improves the system Customer-facing engineering Comfortable working directly with technically sophisticated customers and stakeholders Translate operational problems into engineering solutions, ask the right questions, handle ambiguity, and communicate technical trade-offs clearly Customer interaction is required, but primary responsibility remains engineering and shipping production systems Ownership Operate effectively with significant autonomy Work through difficult deployments, integration failures, or unexpected agent behavior until there is a reliable technical outcome Make decisions under pressure without requiring constant escalation to engineering leadership What Sets You Apart Production agentic systems AI evaluation frameworks AI observability or tracing Voice AI or conversational systems Workflow automation B2B SaaS and enterprise software Regulated industries Complex third-party integrations High-volume customer-facing systems Turning bespoke implementations into reusable platform capabilities Fast-growing product engineering organizations The Environment Building rather than advising High ownership and difficult production problems Customer-facing technical work with fast iteration Small, highly capable teams and ambiguous problems Shipping systems used in real operational workflows Comfort discussing customer workflows, debugging API integrations, reviewing agent traces, improving evaluation suites, and writing production code to solve the problem Location and Working Style Location: Boston, Massachusetts or San Francisco Bay Area Full-time hybrid position Candidates should be based in or able to work from either the Boston or San Francisco Bay Area office, with approximately two days per week in the office Remaining working time may be remote Compensation Competitive Staff-level compensation package including salary, equity, and company benefits. Full details discussed with qualified candidates during the Morena screening process. Show more Show less