Hire and build: Set the technical bar for ML roles on your team, lead or oversee technical assessments, and make hiring decisions you can stand behind.
Build a team that raises the practice's overall standard.
Develop people: Run regular structured 1:1s, provide candid feedback at meaningful milestones, and actively invest in each person's growth — whether they are early in their career or highly experienced.
Manage performance: Recognize strong contributors and address performance gaps directly and early.
Lead ML assessments: Evaluate customer environments end-to-end — infrastructure, data pipelines, model lifecycle, and organizational readiness — and produce recommendations that drive executive decisions and open the door to the next engagement.
Advise on ML operations: Help customers build ML systems they can actually own and sustain — translating MLOps, LLMOps, and production monitoring complexity into standards their engineering teams can execute and their leadership can act on.
Drive pre-sales: Partner with sales and solutions teams during scoping and proposal phases, contributing the technical depth needed to scope work accurately and give prospects confidence in Caylent's ability to deliver. Hands-On Delivery
Lead engagements end-to-end: Drive architecture and solution design from kickoff through delivery — setting technical direction, unblocking the team on hard problems, and ensuring the work meets Caylent's quality standards.
Own the technical relationship: Depending on the engagement, you are either the primary client contact owning all architect-level outcomes, or the senior technical authority providing oversight across the team.
Requirements
Caylent is a cloud native services company that helps organizations bring the best out of their people and technology using Amazon Web Services (AWS).
We provide a full-range of AWS services including workload migrations and modernization, cloud native application development, DevOps, data engineering, security and compliance, and everything in between.
This is a senior role for someone who leads from both directions at once — deeply technical on customer engagements, and fully accountable for the growth and performance of a team of ML engineers and architects.
Partner with HRBPs and the Director of AI/ML when situations require a structured path, and advocate for your team when they deserve it.
Raise the bar internally: Mentor engineers and architects through real work, contribute to technical interviews, and build reference architectures and accelerators that make the broader ML practice better. Your
experience — hiring, performance calibration, career development, and the ability to have difficult conversations directly and constructively.
Deep, current knowledge of the AWS ML and GenAI ecosystem, with the ability to make and defend architectural decisions across the full ML lifecycle — from data and feature engineering through training, deployment, and monitoring.
Deep expertise in at least two or three ML domains — whether classical ML, computer vision, NLP, time series, or others — combined with the judgment to assess, architect, and advise across the broader ML landscape.
Proven ability to architect and govern production ML systems end-to-end, translating MLOps, LLMOps, and broader AI operations complexity into standards that engineering teams can execute and executives can act on.
Deep expertise across foundation model adaptation — fine-tuning (LoRA, QLoRA, PEFT), alignment (RLHF, DPO), inference optimization, and distributed training — combined with RAG and agentic system design, including multi-agent architectures, MCP integration, and human-in-the-loop patterns on AWS.
Proven ability to operate independently in complex, ambiguous customer environments — navigating competing priorities, aligning stakeholders, and translating ML tradeoffs into business risk and value for both technical and executive audiences.
Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements.
Deep fluency in responsible AI practices — model evaluation, bias detection, fairness frameworks, and AI governance — applied in enterprise deployments.
Fluency in AIOps patterns — designing agentic workflows for anomaly detection, automated root cause analysis, and remediation across observability platforms — and the ability to translate AI operations outcomes into measurable business value for customers. Technical Stack
Our practice spans a broad range of ML domains. Candidates are expected to prescribe — not just recognize — with the judgment to maximize what AWS makes possible and the
experience to know how open-source tooling strengthens it.
ML Domains: Classical ML, Computer Vision, NLP, Generative AI & LLMs, AI Agents & Autonomous Systems, Intelligent Document Processing, Video Understanding, Speech & Audio, Time Series & Forecasting, Recommender Systems, Graph ML, Reinforcement Learning, Multimodal AI
AWS ML Platform: SageMaker, SageMaker Pipelines, SageMaker Feature Store, SageMaker Model Registry, SageMaker Clarify, Bedrock (Agents, Knowledge Bases, Guardrails, AgentCore, Model Evaluation)
Multi-provider LLM: Bedrock, Anthropic API, OpenAI API, Google Gemini API, Azure OpenAI — with the judgment to reason across provider tradeoffs in enterprise contexts
AWS AI Services: Rekognition, Comprehend, Transcribe, Textract, Translate, Personalize, Neptune, Kinesis Video Streams, Polly
Data Platform: Apache Spark / PySpark, Apache Kafka, Amazon Kinesis, Apache Iceberg, Delta Lake, Apache Hudi, AWS Glue
As part of our recruitment process, we may use artificial intelligence (AI) tools or automated systems to assist with the screening and evaluation of applications to help match candidate
If an AI or automated tool is used during your application process, it will only be in accordance with applicable laws and regulations, and your information will be handled in a secure and confidential manner.
Experience
10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacity.
Benefits
Inference & Optimization: Triton, vLLM, SGLang, Trainium, Inferentia, Quantization (GPTQ, AWQ, bitsandbytes), SageMaker Neo Benefits 100% remote work Flexible Time Off
Competitive phantom equity
Paid for exams and certifications Peer bonus awards
Equipment & Office Stipend
Annual stipend for Learning and Development
NOTE: We’re unable to provide visa sponsorship now or at any time in the future.
Contact
If you have any questions, please contact talent@caylent.com
If you would like to request an accommodation due to a disability, please contact us at hr@caylent.com.
Additional details
At Caylent, our people always come first. We are a global company and operate fully remote with employees in Canada, the United States, and Latin America.
We celebrate the culture of each of our team members and foster a community of technological curiosity.
Come talk to us to learn more about what it means to be a Caylien! The Mission
You own hiring, development, and team health alongside leading complex customer engagements, shaping architecture, and driving pre-sales.
The right candidate will find energy in that combination, not tension. Your Assignment Leading Your Team
Stay close to staffing: Understand how your team is utilized across engagements, keep the staffing team informed of each person's skills evolution and preferences, and ensure people are placed in work that stretches them appropriately. Strategic Advisory
Shape architecture: Serve as the senior technical authority on engagements, setting architectural direction, ensuring technical quality across the team, and making the calls that matter when tradeoffs are hard.
The expectation is the same in both cases — you are the person the engagement depends on technically.
Exposure to varied industries and problem types in a consulting or client-facing context.
Work with an amazing worldwide team and in an incredible corporate culture This role may require up to 25% travel, depending on business needs.