AI/ML Engineering Manager

Caylent; Brasil; Presencial; BRL 399 201 - 598 802; Tempo integral

Vantagens oferecidas por esta oferta de emprego

Flexible Time Off

Paid exams and certifications

Peer bonus awards

Annual stipend for Learning and Development

State of the art laptop and tools

Resumo da oferta

Caylent is looking for a Senior Machine Learning Lead to manage a team of ML engineers and architects. The role focuses on leading customer engagements, ensuring high-quality standards in ML solutions, and driving pre-sales initiatives. The ideal candidate must have over 10 years of experience in machine learning, proven people management skills, and extensive knowledge of the AWS ML ecosystem. Additional attributes include the ability to operate in complex customer environments and strong fluency in responsible AI practices. This position offers a fully remote work setup.

Qualificações

• 10+ years in machine learning or AI with proven client-facing engagement.
• Demonstrated people-management experience and ability to have difficult conversations.
• Deep knowledge of AWS ML and GenAI, architectural decision-making across ML lifecycle.

Responsabilidades

• Lead team of ML engineers and architects on customer engagements.
• Shape architecture and advise on ML operations to ensure customer ownership.
• Drive pre-sales contributions and technical depth during proposal phases.

Conhecimentos

Machine learning or AI experience

People management

AWS ML and GenAI ecosystem knowledge

Architecting production ML systems

Responsible AI practices

Formação académica

AWS Certified Machine Learning – Specialty

AWS Certified Solutions Architect – Professional

Ferramentas

MLflow

Docker

Kubernetes

Descrição da oferta de emprego

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.

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

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.

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.
• 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
• Flexible Time Off
• 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.

Equal Opportunity Employer

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].

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