Staff Applied AI Engineer, Agents

About the role

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.