ML Practice Manager at Caylent | AWS GenAI Leadership

Caylent; United States; Remote; USD 180,000 - 260,000; Full time

Benefits offered by this job

Generous holidays and flexible PTO

Paid for exams and certifications

Equipment & Office Stipend

Annual Learning & Development stipend

Job summary

Caylent is seeking a senior ML leader who combines hands-on technical leadership with people management. The role oversees ML engineers and architects, directs complex client engagements, and drives pre-sales with executive-level insight.

You will mentor teams, shape architecture, and grow the practice while working remotely for a global client base. 25% travel may be required, with a strong focus on delivering production-grade AI solutions.

Qualifications

• 10+ years in ML/AI with client-facing engagements.
• Deep AWS GenAI knowledge and architectural decision authority.
• Expertise across multiple ML domains and production ML systems.

Responsibilities

• Lead ML engagements end-to-end, directing architecture and delivery.
• Mentor engineers and architects; build accelerators and references.
• Drive pre-sales with technical depth and executive-level communication.

Skills

ML leadership

People management

AWS GenAI knowledge

Architectural decisions

LLM/Agentic systems

Tools

AWS SageMaker

Anthropic Claude

ML Ops tools

Job description

A rare dual-track senior role that combines hands-on technical leadership with genuine people management — not a "player-coach" role in name only. Caylent explicitly states both sides are "real and ongoing," which means this is a substantive management position for someone with 10+ years of ML experience.

Compensation context: No salary range is listed, but roles of this seniority (Staff/Principal ML Architect + Manager) at AWS-partner consulting firms typically command $180,000–$260,000+ USD annually in the US market. USD pay for LATAM-based candidates represents a significant purchasing power advantage. Phantom equity adds upside but is less liquid than standard options — worth understanding the vesting and liquidation terms.

• Enterprise GenAI/LLMOps architecture — one of the fastest-growing and highest-paying specializations in 2024–2025.
• Agentic AI system design with multi-agent architectures and MCP integration — cutting-edge and deeply differentiated.
• Executive stakeholder communication and pre-sales — skills that compound into principal consultant or CTO trajectories.
• Cross-industry ML consulting exposure, which accelerates pattern recognition far faster than single-company roles.

Green flags: AWS Premier Partner status signals high-volume, complex engagements. Anthropic charter partner membership means early access to Claude capabilities. The emphasis on "individual professional development plans" and paid certifications suggests genuine investment in growth.

Watch for: Consulting roles with 25% travel and dual management/IC accountability can trend toward overload. Clarify how team size and staffing are managed during high-demand periods before accepting.

Caylent is an AI-first cloud services company that helps organizations turn ambitious ideas into meaningful business impact. As an AWS Premier Tier Services Partner and a charter member of Anthropic's Claude Partner Network, we combine deep expertise in AWS, artificial intelligence, and Anthropic's Claude platform to help customers modernize their technology, build intelligent products, and move AI from experimentation into production.

Our capabilities span generative and agentic AI, cloud migration and modernization, cloud-native application development, data and analytics, DevOps, managed services, security and compliance, and customer experience transformation. At Caylent, our people always come first.

We are a fully remote global company 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.

The Mission

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. You will report to the Director of AI/ML.

You own hiring, development, and team health alongside leading complex customer engagements, shaping architecture, and driving pre‑sales. Both parts of this job are real and ongoing. The right candidate will find energy in that combination, not tension.

Your Assignment

Leading Your Team

• 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. Partner with HRBPs and the Director of AI/ML when situations require a structured path, and advocate for your team when they deserve it.
• 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

• 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.
• 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.
• 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. The expectation is the same in both cases — you are the person the engagement depends on technically.

Growing the Practice

• 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 Qualifications (non-negotiables)

• 10+ years in machine learning or AI, with a proven track record of leading client-facing engagements in a consulting or advisory capacity.
• Demonstrated people management 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.

Strong Differentiators

• AWS Certified Machine Learning – Specialty and/or AWS Certified Solutions Architect – Professional.
• Experience shaping practice-level standards, reference architectures, and reusable ML accelerators across multiple engagements.
• Exposure to varied industries and problem types in a consulting or client-facing context.
• 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
• Multi-provider LLM: Bedrock, Anthropic API, OpenAI API, Google Gemini API, Azure OpenAI — with the judgment to reason across provider tradeoffs in enterprise contexts
• Vector Databases: Pinecone, pgvector, Amazon OpenSearch (vector), Weaviate
• MLOps & Governance: MLflow, W&B, Airflow / MWAA (data orchestration), Dagster (asset-based pipelines), Kubeflow Pipelines, CI/CD, IaC (CloudFormation, CDK, Terraform), Docker, Kubernetes, ML Governance (lineage, data contracts, audit), Responsible AI / Bias & Fairness
• LLM Evaluation & Safety: RAGAS, LLM-as-judge patterns, DeepEval, NeMo Guardrails, Constitutional AI patterns, structured output validation
• Inference & Optimization: Triton, vLLM, SGLang, Trainium, Inferentia, Quantization (GPTQ, AWQ, bitsandbytes), SageMaker Neo
• Pay in USD
• Generous holidays and flexible PTO
• Paid for exams and certifications
• Peer bonus awards
• State of the art laptop and tools
• Equipment & Office Stipend
• Annual stipend for Learning and Development
• Work with an amazing worldwide team and in an incredible corporate culture

This role may require up to 25% travel, depending on business needs.

NOTE: We're unable to provide visa sponsorship now or at any time in the future.

At Caylent, we are committed to fair, transparent, and inclusive hiring practices. 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 qualifications with job requirements. These tools are designed to support — not replace — human decision-making. Final hiring decisions are always made by our trained recruitment professionals. 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. If you have any questions, please contact [e-mail] .

Caylent is a place where everyone belongs. We celebrate diversity and are committed to creating an inclusive environment for all employees. Our approach helps us to build a winning team that represents a variety of backgrounds, perspectives, and abilities. So, regardless of how your diversity expresses itself, you can find a home here at Caylent.

We are proud to be an equal opportunity employer. We prohibit discrimination and harassment of any kind based on race, color, religion, national origin, sex (including pregnancy), sexual orientation, gender identity, gender expression, age, veteran status, genetic information, disability, or other applicable legally protected characteristics. If you would like to request an accommodation due to a disability, please contact us at [e-mail] .

Is this role fully remote or does it require being in a specific office location? This role is 100% remote. Caylent is a fully remote global company with employees across the United States, Canada, and Latin America, and there is no office requirement. How much travel is involved in this position? The role may require up to 25% travel depending on business needs, which equates to roughly one week per month on average. Travel is tied to client engagements and business demands. Does Caylent offer visa sponsorship for this role? No — Caylent explicitly states they are unable to provide visa sponsorship now or at any time in the future. Candidates must have existing work authorization in their country of residence. What experience level is required for this role? This is a senior role requiring 10+ years in machine learning or AI, including demonstrated people management experience (hiring, performance calibration, career development) and a track record of leading client‑facing consulting or advisory engagements. What AWS certifications would strengthen my application? AWS Certified Machine Learning – Specialty and AWS Certified Solutions Architect – Professional are listed as strong differentiators. Caylent also pays for exams and certifications, so candidates can pursue these after joining.

Remote work & career advice that actually works.