Remote AI/ML Engineering Manager

1w1 week ago

Caylent

MX · Full-time · $150,000 – $220,000

About this role

Caylent is a cloud native services company specializing in AWS, offering workload migrations, cloud native development, DevOps, data engineering, security, and compliance. This senior role leads both technically on customer engagements and manages a team of ML engineers and architects, reporting to the Director of AI/ML. The position combines hands-on delivery with team growth responsibilities.

You will own hiring, development, and performance management while leading ML assessments, shaping architecture, and driving pre-sales efforts. Stay involved in staffing to match team skills to engaging projects. Balance strategic advisory on MLOps and LLMOps with end-to-end engagement leadership.

Caylent operates fully remote across Canada, the United States, and Latin America, prioritizing people first. The company celebrates each team member's culture and fosters technological curiosity. Join a global community where employees thrive in a supportive environment.

Grow the AI/ML practice by building high standards in hiring and delivery. Find energy in leading from both directions—technical depth and team accountability. Partner with sales, solutions, and HR to elevate the team's impact and customer success.

Requirements

Deeply technical expertise in ML engineering for customer engagements
Proven ability to lead and grow teams of ML engineers and architects
Experience shaping ML architecture and making critical tradeoffs
Strong knowledge of MLOps, LLMOps, and production monitoring
Track record leading complex end-to-end ML customer engagements
Skills in conducting ML assessments and producing executive recommendations
Ability to drive pre-sales technical contributions with sales teams
Familiarity with AWS cloud services and cloud native 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 personalized growth investments
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, ensuring quality and resolving tradeoffs
Advise on ML operations, translating MLOps and LLMOps into sustainable standards
Drive pre-sales by contributing technical depth to scoping and proposals
Lead engagements end-to-end, owning technical relationships and solution delivery