AI/ML Engineering Manager
New
C
CaylentCloud Services
MEXICOFull-TimeManager
Salary not disclosed
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Job Details
- Experience
- 10+ years
- Required Skills
- AWSDockerArtificial IntelligenceKubernetesMachine LearningMLFlowPyTorchAirflowApache KafkaSparkTensorflowCI/CDTerraformCloudFormationscikit-learnGenerative AILangChainPySpark
Requirements
- 10+ years in machine learning or AI
- Proven track record of leading client-facing engagements in a consulting or advisory capacity
- Demonstrated people management experience (hiring, performance calibration, career development)
- Deep, current knowledge of the AWS ML and GenAI ecosystem
- Ability to make and defend architectural decisions across the full ML lifecycle (data, feature engineering, training, deployment, monitoring)
- Deep expertise in at least two or three ML domains (classical ML, computer vision, NLP, time series, etc.)
- Proven ability to architect and govern production ML systems end-to-end
- Expertise across foundation model adaptation (fine-tuning, LoRA, QLoRA, PEFT, alignment, RLHF, DPO)
- Expertise in inference optimization and distributed training
- Expertise in RAG and agentic system design (multi-agent architectures, MCP integration, human-in-the-loop patterns on AWS)
- Proven ability to operate independently in complex, ambiguous customer environments
- Experience with AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional (strong differentiator)
- Experience shaping practice-level standards, reference architectures, and reusable ML accelerators (strong differentiator)
- Exposure to varied industries and problem types in a consulting or client-facing context (strong differentiator)
- Fluency in responsible AI practices (model evaluation, bias detection, fairness frameworks, AI governance) (strong differentiator)
- Fluency in AIOps patterns (designing agentic workflows for anomaly detection, automated root cause analysis, remediation) (strong differentiator)
Responsibilities
- Set the technical bar for ML roles on your team, lead or oversee technical assessments, and make hiring decisions
- Run regular structured 1:1s, provide candid feedback, and actively invest in each person's growth
- Recognize strong contributors and address performance gaps directly and early, partnering with HRBPs and the Director of AI/ML when needed
- Understand how your team is utilized across engagements, keep the staffing team informed of each person's skills evolution and preferences
- Evaluate customer environments end-to-end (infrastructure, data pipelines, model lifecycle, organizational readiness) and produce recommendations
- Serve as the senior technical authority on engagements, setting architectural direction and ensuring technical quality
- Help customers build ML systems they can actually own and sustain, translating MLOps, LLMOps, and production monitoring complexity into standards
- Partner with sales and solutions teams during scoping and proposal phases, contributing technical depth
- Drive architecture and solution design from kickoff through delivery, setting technical direction and unblocking the team
- Own the technical relationship with clients, either as primary contact or senior technical authority
- Mentor engineers and architects, contribute to technical interviews, and build reference architectures and accelerators
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