Solutions Architect
H
HumanSignalAI Data Infrastructure
Across North AmericaFull-TimeSenior
SalaryBase Salary is targeted between $122,500 - 143,500 USD. This role also qualifies for variable compensation; anticipated On-Target Earnings if an employee is meeting objectives are $175,000 - $205,000 USD.
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Job Details
- Experience
- 5+ years
- Required Skills
- DockerPythonJavascriptKubernetesMachine LearningRESTful APIsLinuxSaaS
Requirements
- 5+ years in a customer-facing technical role (Solutions Architect, Sales Engineer, Professional Services Engineer) for enterprise SaaS or ML/AI platforms.
- Direct experience building data labeling pipelines, labeling workflows, or supporting the broader ML lifecycle.
- Hands-on experience integrating SaaS platforms with ML pipelines and deploying models to production.
- Fluency in Python, REST APIs, and infrastructure tools (Linux/Unix, Docker).
- Experience running technical discovery, demos, and proof-of-concepts during the sales cycle.
- Proven ability to own post-sales implementation, troubleshooting, and custom development.
- Strong communication skills with the ability to bridge technical requirements and business value propositions.
- Executive presence and professional writing skills.
- Experience managing multiple complex accounts and competing priorities.
- Kubernetes knowledge is a plus.
- Proficiency in JavaScript, CSS, and HTML is a plus.
Responsibilities
- Partner with Account Executives on strategic deals, leading technical discovery, tailored demos, architecture reviews, and proof-of-concepts.
- Drive technical onboarding, guiding customers through installation, secure configuration, and best-practice deployment across cloud, on-prem, or hybrid environments.
- Architect integrations between Label Studio and customer AI/ML workflows, pipelines, storage, and enterprise systems.
- Build custom solutions (scripts, plug-ins, and APIs) to extend Label Studio for unique customer requirements.
- Serve as the senior technical escalation point for your accounts, resolving advanced issues and collaborating with Product, Engineering, and Support.
- Deliver enablement, workshops, and documentation that make customer teams self-sufficient on the platform.
- Act as a trusted technical advisor to customer engineering, data, and AI/ML teams, supporting adoption, expansion, and long-term value.
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