Senior AI/ML Systems Architect for Enterprise Production

TechVerse Solutions; United States; Remote; USD 180,000 - 230,000; Full time

Job summary

TechVerse Solutions is seeking a senior individual contributor to design and ship production AI systems for enterprise clients. You will work across the full ML stack—from data pipelines to model training, deployment, and monitoring—combining hands-on engineering with technical leadership.

You will own key technical decisions, mentor engineers, and present to client CTOs and senior leaders, translating business problems into scalable AI/ML solutions that meet reliability and performance

Qualifications

• Senior-level experience building and operating AI/ML systems in production.
• Strong working knowledge across the ML lifecycle: data engineering, model development, deployment, observability.
• Proven ability to make and defend architectural decisions on complex AI projects.
• Demonstrated client-facing or stakeholder communication skills, including presenting to technical executives.
• Track record of mentoring engineers and contributing to team technical culture.

Responsibilities

• Architect and deliver end-to-end ML systems, including data pipelines, model training, deployment, and production monitoring.
• Own key technical decisions and serve as the primary technical contact during presentations to client CTOs and senior engineering leaders.
• Mentor junior engineers and help raise the technical bar across the team.
• Translate ambiguous business problems into well-scoped AI/ML solutions that meet enterprise reliability and scale requirements.
• Collaborate directly with stakeholders to align model design, performance, and operational trade-offs with business outcomes.

Skills

Senior AI/ML systems

ML lifecycle knowledge

Architectural decisions

Client-facing communication

Mentoring engineers

Job description

TechVerse Solutions is seeking a senior individual contributor to design and ship production AI systems for enterprise clients. You will work across the full ML stack—from data pipelines to model training, deployment, and monitoring—combining hands-on engineering with technical leadership.

You will own key technical decisions, mentor engineers, and present to client CTOs and senior leaders, translating business problems into scalable AI/ML solutions that meet reliability and performance