- Company Name
- Maki People
- Job Title
- AI deployment architect
- Job Description
-
Job title: AI Deployment Architect
Role Summary: Design, configure, and deploy AI‑driven screening agents at enterprise scale, translating business requirements into robust, maintainable setups. Act as the primary technical partner for customers, troubleshoot performance issues, iterate rapidly, and provide feedback to product and engineering.
Expectations:
- Deliver high‑performance, reliable AI deployments that meet client specifications.
- Serve as a trusted advisor, guiding customers through configuration, demos, and pilot execution.
- Maintain clear communication with technical and non‑technical stakeholders.
- Drive continuous improvement by iterating on prompts, rules, and system settings.
- Contribute field insights to shape product roadmap and platform enhancements.
Key Responsibilities:
1. Configuration & Deployment
- Build and adapt screening flows for specific roles, jobs, and needs.
- Set state prompts, tone, voice, transitions, and conditional logic.
- Configure custom vocabularies for ASR where required.
- Prepare, present, and support pilot demos from initiation to completion.
2. Troubleshooting & Iteration
- Analyze transcripts to locate errors, drift, or performance regressions.
- Conduct isolated state tests and targeted debugging.
- Iterate on prompts/configurations, validate improvements with SQL analysis.
3. Client Partnership
- Advise on screening design, personas, and best practices.
- Translate technical concepts, trade‑offs, and limitations to stakeholders.
- Manage pilot expectations, structure feedback loops, and monitor progress.
4. Product Collaboration
- Report recurring field issues for productization.
- Provide insights for new configuration surfaces, evaluation tools, and system features.
- Test new product features with selected customers and assess readiness for scale.
Required Skills:
- Hands‑on experience with conversational AI, LLM prompting, or workflow‑based configuration.
- Ability to debug state logic, model outputs, and configuration inconsistencies.
- SQL proficiency for analytical investigations of conversational data.
- Familiarity with scripting and structured configuration formats.
- Strong analytical and product mindset; ability to differentiate between local fixes and product‑level features.
- Structured experimentation and rapid iteration skills.
- Excellent written and verbal communication; ability to educate and manage expectations.
- Proven client‑facing technical experience, ideally in enterprise implementation.
Required Education & Certifications:
- Bachelor’s or Master’s degree from a top engineering school or dual‑degree engineering/business program.
- Any relevant certifications in AI, machine learning, or product management are a plus but not mandatory.