AI/ML Engineering Manager - Remote

1w1 week ago

Caylent

AR · Full-time · $190,000 – $260,000

About this role

Caylent is a cloud native services company specializing in AWS services like workload migrations, modernization, cloud native application development, DevOps, data engineering, security, and compliance. This senior role leads from both directions: deeply technical on customer engagements and accountable for team growth and performance of ML engineers and architects. You report to the Director of AI/ML and own hiring, development, team health, complex engagements, architecture, and pre-sales.

Lead your team by setting the technical bar for ML roles, overseeing assessments, making hiring decisions, running 1:1s, providing feedback, managing performance, and staying close to staffing. Provide strategic advisory through ML assessments of infrastructure, data pipelines, model lifecycle, shaping architecture as senior authority, advising on MLOps and LLMOps, and driving pre-sales with sales teams.

Handle hands-on delivery by leading engagements end-to-end, driving architecture and solution design, unblocking teams, and owning the technical relationship as primary client contact or senior oversight. Caylent operates fully remote with a global team in Canada, the United States, and Latin America, putting people first and celebrating each member's culture.

Grow the practice by building a team that raises standards, investing in individual growth from early career to experienced levels, and partnering with HRBPs and leadership. The right candidate thrives on the energy of combining technical leadership with team management, fostering a community of technological curiosity.

Requirements

Deeply technical expertise in ML engineering and architecture for customer engagements
Proven leadership of ML engineering teams including hiring, development, and performance management
Experience shaping ML architecture and making decisions on complex tradeoffs
Knowledge of MLOps, LLMOps, data pipelines, model lifecycle, and production monitoring
Ability to lead end-to-end ML engagements from assessments to delivery
Skills in pre-sales technical contributions for accurate scoping and proposals
Familiarity with AWS cloud native services and infrastructure evaluation
Track record fostering team health and staffing optimization in ML practices

Responsibilities

Hire and build a team of ML engineers and architects by setting the technical bar and leading assessments
Develop people through regular 1:1s, candid feedback, and investing in growth at all career stages
Manage performance by recognizing contributors, addressing gaps early, and advocating for the team
Lead ML assessments evaluating infrastructure, data pipelines, model lifecycle, and organizational readiness
Shape architecture as senior technical authority on engagements and make key tradeoff decisions
Advise on ML operations including MLOps, LLMOps, and production monitoring for sustainable systems
Drive pre-sales by partnering with sales on scoping, proposals, and technical confidence
Lead engagements end-to-end from kickoff through delivery, owning technical relationships