Role Summary:As a Senior Forward Deployed Engineer at Maven AGI, you will own post-sale technical implementation for strategic enterprise customers. You will work directly with customer teams to turn ambiguous support workflows into production AI systems: scoping requirements, building integrations, configuring Maven, debugging edge cases, validating quality, launching safely, and iterating from real-world usage.
This is not a demo-only or pre-sales engineering role. You should be a hands-on builder who is comfortable in front of customers and equally comfortable writing code, working with APIs, reading logs, reasoning through data flows, and making practical tradeoffs under delivery pressure.
What You'll Do:- Own post-sale implementation workstreams from kickoff through production launch and early customer success.
- Translate messy customer support workflows into concrete technical plans, milestones, risks, and launch criteria.
- Build and debug integrations across systems such as CRMs, ticketing platforms, CCaaS tools, knowledge bases, data warehouses, identity providers, and internal customer APIs.
- Configure and extend Maven using JavaScript, TypeScript, Node.js, APIs, webhooks, and the Maven SDK.
- Prototype quickly when customer requirements are ambiguous, then turn the right parts into reliable production implementation.
- Design and run practical quality checks for AI behavior, including answer quality, retrieval quality, action correctness, escalation behavior, and regression risks.
- Set up launch readiness plans across testing, observability, logging, fallback behavior, rollback paths, and customer signoff.
- Partner with Product and Engineering when field work reveals platform gaps, repeated customer needs, or reusable implementation patterns.
- Communicate clearly with customer operators, technical teams, executives, and internal Maven teams throughout the deployment.
What Success Looks Like:- Customers launch Maven into production with clear scope, strong technical execution, and measurable support impact.
- Implementation risks are surfaced early, owned directly, and resolved with practical technical judgment.
- AI workflows are tested, monitored, and improved using real quality signals rather than guesswork.
- Customer-specific work turns into reusable patterns, documentation, templates, and product feedback for future deployments.
- Customer teams trust you because you make ambiguous problems concrete and follow through technically.
RequirementsWho You Are:- 5+ years of software engineering, implementation engineering, forward deployed engineering, solutions engineering, partner engineering, or equivalent customer-facing technical delivery experience.
- Strong hands-on coding ability in JavaScript/Node.js. TypeScript, React, Next.js, and Python experience are strong pluses.
- Strong API and integration judgment across REST APIs, webhooks, auth, data mapping, retries, error handling, and operational debugging.
- Experience taking complex customer-facing technical projects from ambiguous requirements into production.
- Comfortable working directly with customers, asking sharp discovery questions, explaining tradeoffs, and pushing back constructively when scope or timeline is risky.
- Practical understanding of AI/LLM systems, including prompting, retrieval, tool/action use, evals, failure modes, and quality measurement.
- Strong written and verbal communication. You can write a clear implementation plan, technical memo, customer update, or launch checklist.
- High ownership. You do not wait for perfect requirements; you identify the next useful step, make progress, and keep the customer and team aligned.
Nice To Haves:- Experience with customer support systems such as Salesforce Service Cloud, Zendesk, ServiceNow, Intercom, or similar platforms.
- Experience with CCaaS or voice platforms such as Genesys, Five9, NICE, Twilio, Amazon Connect, or similar systems.
- Experience with data integration, ETL, or workflow automation tools such as Workato, Airbyte, Fivetran, or similar systems.
- Experience with observability, CI/CD, incident response, and production support practices.
- Knowledge of security, privacy, and compliance considerations such as SOC 2, GDPR, HIPAA, least-privilege access, audit logging, and data retention.
- Experience at an AI company, high-growth SaaS company, implementation-heavy product company, or field engineering organization.
BenefitsWhat We Offer:- High-impact work in applied AI: build production systems that change how enterprise support teams operate.
- Competitive salary, comprehensive benefits, and meaningful equity.
- A team that values direct ownership, customer impact, technical rigor, and fast learning.
