scikit-learn Jobs in Mexico.
All active scikit-learn roles based in Mexico.
Pick a job to read the details
Tap any role on the left — its description and apply link will open here.
Sr. Solutions Engineer
Share this job
About Databricks
Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook.
Benefits
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here.
Our Commitment to Diversity and Inclusion
At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.
Compliance
If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.
Ready to apply?
Apply to Databricks
Contract: AI Operations Specialist
Share this job
Why You'll Love This Role:
We're looking for an experienced ML Ops Engineer to join the ML/AI team at Newsela. This team works on projects ranging from classical Machine Learning to AI / Generative pipelines. This is a hands-on role. You'll work closely with ML/AI, data and site reliability engineers to take models from prototype to production, build robust data pipelines, and keep our services running smoothly as we continue to scale.
What You'll Be Doing:
- Design and maintain CI/CD pipelines for ML model training, packaging, and deployment across our microservices.
- Manage containerized services on AWS ECS, optimizing for cost, latency, and availability.
- Automate infrastructure provisioning and service configuration with Terraform.
- Work to maintain and scale services that make use of third party LLM providers.
- Build and improve data pipelines that feed models from BigQuery, S3, and DynamoDB into training and inference workflows.
- Instrument services with observability tooling (Datadog, OpenTelemetry, Langfuse) and establish SLOs for model-serving endpoints.
- Collaborate with ML engineers to productionize new models using BentoML, FastAPI, and container-based serving.
About You:
- 2-3 years in ML Ops supporting ML/AI features, systems and workflows with 3-4 years prior experience in DevOps, CloudOps or SRE.
- Strong proficiency in Python.
- Hands-on experience with Docker containerization and container orchestration.
- Solid understanding of CI/CD for ML workflows in an enterprise production environment.
- Experience with Infrastructure as Code, preferably Terraform.
- Familiarity with cloud platforms — specifically AWS (ECS, ECR, S3, DynamoDB, CloudWatch) and GCP (BigQuery, Vertex AI).
- Experience with LLM integration and observability (OpenAI API, Google GenAI, Langfuse tracing).
- Experience building and maintaining data pipelines for ML training and feature engineering
- Familiarity with ML modeling workflows — training, evaluation, experiment tracking (e.g. MLFlow, Weights & Biases), and model versioning
- Experience monitoring and flagging model drift over time.
- Exposure to NLP/NLU models and frameworks such as Hugging Face Transformers, spaCy, or sentence-transformers
- Knowledge of vector databases (LanceDB, FAISS) and embedding-based retrieval systems
- Experience with scaling and maintaining deep learning frameworks (TensorFlow, PyTorch) in production settings
- Familiarity with classical ML libraries (scikit-learn, XGBoost, LightGBM) and model explainability tools (SHAP)
- Working knowledge of ML serving frameworks such as BentoML or similar.
- Comfort working with FastAPI or similar async Python web frameworks.
Please note that given the nature of the contract, this role will not be eligible to participate in company-sponsored benefits
About Newsela:
Newsela is a leading education technology company dedicated to meaningful classroom learning for every student. We deliver integrated, AI-powered solutions designed to unlock student engagement, empower teachers, and drive meaningful learning outcomes. Our suite of products supports knowledge and skill development, writing practice, daily instruction, assessment, and data-informed decision-making across K–12 classrooms. Grounded in learning science research, Newsela’s solutions integrate content, assessment, and analytics to help educators track progress, understand student outcomes, and deliver high-impact instruction that supports every learner.
#LI-Remote
Ready to apply?
Apply to Newsela
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.
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
- 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
- Vector Databases: Pinecone, pgvector, Amazon OpenSearch (vector), Weaviate
- Frameworks: PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, HuggingFace (Transformers, PEFT, TRL), LangChain, LlamaIndex, DSPy, Ollama
- 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
Benefits
- 100% remote work
- Medical Insurance for you and eligible dependents
- Generous holidays and flexible PTO
- Competitive phantom equity
- Paid for exams and certifications
- Peer bonus awards
- State of the art laptop and tools
- Equipment & Office Stipend
- Individual professional development plan
- Annual stipend for Learning and Development
- Work with an amazing worldwide team and in an incredible corporate culture
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 talent@caylent.com
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 hr@caylent.com.Ready to apply?
Apply to CaylentThe mission:
The Senior Credit Risk Data Scientist at Baubap is responsible for designing, implementing, and improving predictive models that directly impact our credit decisions and portfolio performance. This role will play a critical part in generating accurate forecasts, building data-driven methodologies, and continuously iterating based on real-world learning—always grounded in a deep understanding of the business context.
The expected outcome:
- Develop, deploy, and maintain machine learning models that improve the accuracy of forecasts across key business levers such as approval rate, disbursement rate, loss rate, and average loan amount.
- Deliver short-, mid-, and long-term forecasts for key portfolio and business indicators to guide strategic decision-making.
- Continuously improve models and data pipelines based on new insights, feedback loops, and shifts in portfolio dynamics.
- Build methodologies that reflect a clear understanding of the business and customer behavior—combining data science best practices with practical, real-world constraints.
The day to day tasks:
- Model development & deployment: Design and implement predictive models (classification, regression, time series, etc.) to forecast credit risk metrics and optimize decision-making.
- Model iteration & lifecycle management: Regularly retrain and improve models based on recent performance, business evolution, and new data availability.
- Forecasting: Build robust models to predict portfolio KPIs over different time horizons (daily/weekly/monthly), including loss rate, disbursed amount, average ticket size, and approval rate.
- Experimentation: Collaborate with cross-functional teams to design and evaluate A/B tests or quasi-experiments that inform modeling improvements.
- Feature engineering: Create high-quality, interpretable features from raw transactional and behavioral data.
- Data exploration & root-cause analysis: Use statistical techniques to detect anomalies, understand shifts in model performance, and identify risks or opportunities.
- Business alignment: Partner closely with Risk, Product, Finance, and Data Engineering teams to ensure that models and methodologies are aligned with business goals and operational realities.
- Documentation & reproducibility: Maintain clear documentation of models, assumptions, and decisions to ensure transparency, auditability, and future scaling.
Why YOU should apply:
- 5+ years of experience developing, validating, and deploying predictive models in a production environment, preferably within financial services or credit risk.
- Strong proficiency in SQL for data extraction and transformation.
- Advanced skills in at least one programming language commonly used in data science, such as Python or R.
- Proven ability to build and tune machine learning models (e.g., classification, regression, time series forecasting), using libraries such as scikit-learn, XGBoost, LightGBM, etc.
- Experience maintaining and iterating on models based on real-world performance and shifting data patterns.
- Comfortable working with experimentation frameworks, A/B testing, and validation pipelines.
- Ability to translate complex technical insights into clear business recommendations.
- Strong understanding of statistical concepts and their application in risk modeling.
- Fluent in English (written and spoken); able to work and communicate effectively with an international and cross-functional team.
- Bonus: experience in financial risk areas
- Bonus: experience working with version control tools (e.g., Git), workflow managers (e.g., Airflow), and cloud-based data platforms (e.g., AWS, GCP, or similar).
What we can offer:
- Being part of a multicultural, highly driven team of professionals
- 20 vacation days / year + 75% holiday bonus (Prima Vacacional)
- 1 month (proportional) of Christmas bonus (Aguinaldo)
- Food vouchers
- Health & Life insurance
- Competitive salary
Ready to apply?
Apply to Baubap
Machine Learning Architect - Not an Active Opening, Building Talent Pipeline
Share this job
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
We are seeking an exceptional Machine Learning Architect to join our growing Cloud Native Applications team. The right candidate is someone who has deep expertise in ML system design and is passionate about working with our customers, partners, and colleagues to drive innovation forward. Your mission will be to work alongside Caylent’s Engineers, Engineering Managers, and Project Managers to deliver AWS solutions across our diverse and forward-thinking customer base. You’ll work with the latest technologies and support customers looking to bring cutting-edge ideas to market.
Your Assignment
- You will be a mission control specialist, guiding Cayliens and Customers alike through Agile ceremonies like stand-ups, retrospectives, and more.
- You will translate customer requirements and into a workable backlog of tickets for engineers.
- Delegate tickets to a team of engineers in order to complete customer projects.
- Lead requirements gathering, backlog grooming, and architecture discussions.
- Apply your understanding of DevOps pipelines, including build automation, branching strategies, CI/CD, Infrastructure as Code, security, monitoring, logging, and alerting.
- Troubleshoot and resolve issues in customer dev, test, and production environments.
- Automate software testing at multiple levels (component, configuration item, subsystem, system) and monitor results.
- Write production quality code, including unit and integration tests.
- Work with a team to deliver top-quality cloud native applications on AWS for customers.
- Clearly communicate and document your designs, processes, and procedures.
- You will demonstrate a passion to Stay Curious as you mentor peers, tackle new technologies, and learn from our word-class team of engineers.
- Coach and mentor less experienced teammates, coach and be mentored by world class engineers.
Your Qualifications
Core Consulting Qualifications
Customer Experience
- Confidently addresses client questions and identifies gaps in requirements with proposed next steps.
- Focus on delivering clear, actionable solutions that meet client goals
Collaboration
- Facilitates alignment across stakeholders or technical priorities and deliverables.
- Navigates differing perspectives with professionalism to ensure shared understanding and progress.
Communication
- Breaks down complex technical concepts for non-technical audiences in a clear, concise manner.
- Adapts communication style based on audience and context to improve clarity and decision-making.
Technical Qualifications
- Experience with IaC tools (CloudFormation, CDK, Terraform).
- Expert level experience in Amazon SageMaker and ML libraries (TensorFlow, MXNet, PyTorch, Scikit-learn).
- Strong understanding of ML concepts: feature engineering, hyperparameter tuning, optimization strategies.
- Familiarity with MLOps tools (MLflow, Neptune, Comet).
Benefits
- 100% remote work
- Medical Insurance for you and eligible dependents
- Generous holidays and flexible PTO
- Competitive phantom equity
- Paid for exams and certifications
- Peer bonus awards
- State of the art laptop and tools
- Equipment & Office Stipend
- Individual professional development plan
- 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 talent@caylent.com
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 hr@caylent.com.Ready to apply?
Apply to Caylent
Cloud Developer --- Hibrido CDMX
Share this job
Orion Innovation is a premier, award-winning, global business and technology services firm. Orion delivers game-changing business transformation and product development rooted in digital strategy, experience design, and engineering, with a unique combination of agility, scale, and maturity. We work with a wide range of clients across many industries including financial services, professional services, telecommunications and media, consumer products, automotive, industrial automation, professional sports and entertainment, life sciences, ecommerce, and education.
Perfil: Desarrollador Cloud
Rol: Desarrollador
Nivel: STD – SR
Experiencia
Años de experiencia:
- 3 o más años de experiencia como Desarrollador/a Cloud (Multi‑Nube) con IA/ML y DevOps.
Escolaridad
- Licenciatura o Ingeniería en:
- Sistemas
- Computación
- Matemáticas
- Física
- Carrera afín
- (O experiencia equivalente comprobable)
Modalidad y Condiciones de Trabajo
- Modalidad de trabajo: Híbrido
- Días en oficina: 2 veces por semana
- Horario: Lunes a viernes, 9:00 a.m. a 6:00 p.m.
- Ubicación: CDMX
- Dirección de sede: Polanco / Reforma
Objetivo del Puesto
Diseñar, construir y operar aplicaciones y servicios escalables en entornos multi‑nube (AWS, GCP y/o Azure), integrando desarrollo backend, prácticas DevOps/MLOps e implementación de modelos de IA/ML en entornos productivos, asegurando calidad, seguridad, observabilidad y eficiencia operativa.
Certificaciones Recomendadas
Google Cloud Platform
- Google Professional Cloud Developer
- Google Professional Cloud Architect (deseable para perfil SR)
- Google Professional Machine Learning Engineer
- Google Associate Cloud Engineer
Amazon Web Services (AWS)
- AWS Certified Developer – Associate
- AWS Certified Solutions Architect – Associate
- AWS Certified Machine Learning – Specialty (altamente alineada a IA/ML)
- AWS DevOps Engineer – Professional (deseable para perfil SR)
Microsoft Azure
- Microsoft Certified: Azure Developer Associate (AZ‑204)
- Microsoft Certified: Azure AI Engineer Associate
- Microsoft Certified: Azure DevOps Engineer Expert
Machine Learning
- Google Professional Machine Learning Engineer
- AWS Certified Machine Learning – Specialty
- Azure Data Scientist Associate
- TensorFlow Developer Certificate
- IBM AI Engineering Professional Certificate
Idioma
- Inglés técnico (deseable)
Habilidades Blandas
- Trabajo en equipo
- Autogestión
- Perfil autodidacta
- Comunicación efectiva
- Resolución de problemas
- Adaptabilidad
- Proactividad
- Pensamiento analítico
Requisitos Técnicos
Versionador e Integrador de Código
- Git
- GitHub Actions / GitLab CI
- Cloud Build / Cloud Deploy (GCP)
- CodeBuild / CodePipeline (AWS)
- Azure DevOps Pipelines
Lenguajes de Programación
- Python
- Node.js
- TypeScript (deseable)
- SQL
- YAML (pipelines / Infraestructura como Código)
Sistemas Operativos
- Linux (principal)
- Windows
- macOS
Manejadores de Bases de Datos
- Cloud SQL
- Amazon RDS
- Azure SQL
- BigQuery
- Bases de datos relacionales y analíticas
- Almacenamiento de objetos:
- Amazon S3
- Google Cloud Storage
- Azure Blob Storage
Frameworks de Desarrollo
Backend
- Express
- Fastify (Node.js)
IA / ML
- scikit-learn
- TensorFlow / Keras
- PyTorch
NLP
- spaCy
- NLTK
- Hugging Face Transformers (deseable)
IDE
- Visual Studio Code
- IDEs compatibles con Python y Node.js
Herramientas
- Docker
- Kubernetes (GKE / EKS / AKS)
- Helm
- Terraform
- MLflow / DVC
- Postman (pruebas de APIs)
- Prometheus / Grafana
- OpenTelemetry
Plataformas Cloud
- Google Cloud Platform (principal)
- Amazon Web Services (deseable)
- Microsoft Azure (deseable)
Asistentes de IA
- No especificados explícitamente
- (Uso implícito de frameworks y plataformas de IA/ML)
Conocimiento del Negocio
Marcos o Metodologías de Trabajo
- Metodología Ágil
- Scrum
Herramientas para Seguimiento y Control de Proyectos
- Gestión de backlog (herramientas ágiles no especificadas)
- Métricas ágiles:
- Velocity
- Lead Time
- Tableros de observabilidad
- Definición y seguimiento de SLO / SLI
Esquema de Trabajo
- Asignación a proyectos
- Desarrollo y operación de productos digitales
- (No esquema de fábrica de software)
Tipo de Asignación
- Proyectos
- Operación
- Soporte productivo
- Mejora continua
Tipo de Proyecto
- Ágil
Industria
- Banca / Servicios Financieros
Tecnología
- Cloud Computing
- Inteligencia Artificial / Data
Orion is an equal opportunity employer, and all qualified applicants will receive consideration for employment without regard to race, color, creed, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, citizenship status, disability status, genetic information, protected veteran status, or any other characteristic protected by law.
Candidate Privacy Policy
Orion Systems Integrators, LLC and its subsidiaries and its affiliates (collectively, “Orion,” “we” or “us”) are committed to protecting your privacy. This Candidate Privacy Policy (orioninc.com) (“Notice”) explains:
- What information we collect during our application and recruitment process and why we collect it;
- How we handle that information; and
- How to access and update that information.
Your use of Orion services is governed by any applicable terms in this notice and our general Privacy Policy.
Ready to apply?
Apply to Orion Innovation
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 Opportunity
This is a senior technical client leadership role that blends deep hands-on ML expertise with strategic advisory and consulting skills. You will be the most experienced ML voice across a diverse and expanding book of customer engagements — from early-stage companies bringing ambitious ML ideas to market, to established enterprises modernizing how they build and operate AI systems on AWS.
You will shape strategy, influence architecture, and leave every team you touch better than you found it. You bring the scientific depth to design and evaluate models rigorously, the engineering depth to architect production ML systems at scale, and the consulting instincts to translate both into business value for customers.
If you have led the hard conversations, shaped the architecture decisions that mattered, and built the things others benchmark against — and you are looking to do that across a growing portfolio of varied and interesting customers — this is the role for you.
What You'll Do
- Lead end-to-end ML assessments across infrastructure, data pipelines, model lifecycle, and organizational readiness — producing recommendations that drive executive decision-making and earn Caylent the next engagement.
- Partner with sales and solutions teams through the proposal and scoping phase, contributing the technical depth needed to shape well-grounded statements of work.
- Serve as the senior technical authority on client engagements — possibly across multiple projects simultaneously — providing architectural guidance, ensuring technical quality from your project team members, and getting hands-on when the engagement demands it, without owning day-to-day implementation responsibilities.
- Own or orchestrate high-quality POCs that give customers confidence before committing to a larger initiative.
- Advise customers on ML operations standards and architecture — covering MLOps pipeline design, model lifecycle management, LLMOps patterns, and production monitoring frameworks — translating operational complexity into decisions and guardrails their teams can own and sustain.
- Shape how Caylent wins its most technically complex opportunities — contributing the architectural thinking and credibility that turns prospects into customers.
- Strengthen the ML practice from the inside — through peer guidance, technical interviews, and contributions to accelerators, reference architectures, and thought leadership content.
What You Bring
The 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.
- 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 traditional 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 and decisions 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 (quantization, vLLM), and distributed training (DeepSpeed, FSDP) — combined with RAG and agentic system design, including multi-agent architectures, event-driven workflows, MCP integration, and human-in-the-loop patterns on AWS. Technical authority to prescribe the right approach and set architectural standards that teams can execute against.
- Proven ability to operate independently in complex customer environments — navigating ambiguity, 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.
- Hands-on experience designing and deploying SRE agents and AI-driven operations workflows in production — spanning automated incident detection, triage, and remediation — with the ability to integrate across observability platforms and translate AI operations outcomes into measurable business value.
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
Vector Databases: Pinecone, pgvector, Amazon OpenSearch (vector), Weaviate
Frameworks: PyTorch, TensorFlow, JAX, Scikit-learn, XGBoost, HuggingFace (Transformers, PEFT, TRL), LangChain, LlamaIndex, DSPy, Ollama
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
Benefits
- 100% remote work
- Private Health Insurance
- Flexible Time Off
- Competitive phantom equity
- Paid for exams and certifications
- Peer bonus awards
- State of the art laptop and tools
- Equipment & Office Stipend
- Individual professional development plan
- Annual stipend for Learning and Development
- Work with an amazing worldwide team and in an incredible corporate culture
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 talent@caylent.com
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 hr@caylent.com.Ready to apply?
Apply to CaylentSenior Machine Learning Engineer - Not an Active Opening, Building Talent Pipeline
Share this job
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!
Note: This isn’t an active role right now, but we’re building a community of great talent for future opportunities at Caylent. If your background aligns with what we’re looking for, our team may reach out to learn more about you and explore potential future fits.
The Mission
At Caylent, a Senior Machine Learning Engineer works as an integral part of a cross-functional delivery team to design and document machine learning solutions on the AWS cloud for our customers. We are looking for someone that has a strong understanding of the various model types and tools, and can help our customers connect their business goals with the details of feature design, model training and inference. You will develop solutions designed by an architect.
You will participate in daily standup meetings with your team and bi-weekly agile ceremonies with the customer. Your manager will have a weekly 1:1 with you to help guide you in your career and make the most of your time at Caylent.
Your Assignments
- Work with a team to deliver machine learning solutions on AWS for customers
- Participate in and contribute to daily standup meetings
- Develop and implement ML models, MLOps, and analytics
- Big data processing and preparation of training data for models
Your Qualifications
- Strong experience in building ML models for real world applications
- Strong experience in at least one of these:
- AWS ML Services/SageMaker
- ML libraries like Keras, Tensorflow, PyTorch, Scikit-learn
- MLOps tools such as MLflow, Kubeflow, Airflow
- Advanced analytics using time series forecasting and/or inferential statistics
- Strong experience in one or more of these data processing solutions:
- Big data processing platforms like Spark, Hadoop, or streaming platforms
- Data processing and cleansing using Python/Pandas, PySpark, Scala, SQL
- Strong understanding of feature definition, model meta-data, hyperparameter tuning, stochastic gradient descent, deep learning layer types and activation functions
- Experience in visualization using SageMaker, ggplot, matplotlib, or seaborn
- Experience with an IaC tool such as CloudFormation, Amazon CDK or Terraform
- Excellent written and verbal communication skills
Benefits
- 100% remote work
- Medical Insurance for you and eligible dependents
- Generous holidays and flexible PTO
- Competitive phantom equity
- Paid for exams and certifications
- Peer bonus awards
- State of the art laptop and tools
- Equipment & Office Stipend
- Individual professional development plan
- 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 talent@caylent.com
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 hr@caylent.com.Ready to apply?
Apply to CaylentAbout Us
Nu was born in 2013 with the mission to fight complexity to empower people in their daily lives by reinventing financial services. We are one of the world’s largest digital banking platforms, serving millions of customers across Brazil, Mexico, and Colombia.
About the role
We are seeking an enthusiastic and driven Data Scientist to join our Credit Strategy team. This role focuses on applying innovative machine learning methodologies to our risk model and developing causal machine learning approaches for the initial line assignment workflow.
You will be responsible for
- Translate business challenges into specific and well-designed machine learning solutions that meet business requirements;
- Integrate and leverage external data sources to feed our models;
- Ensure the team maintains a high level of technical excellence;
- Contribute to a technical culture in the local team.
- Bring expertise to the team to help design projects across the many areas involved in building machine learning products (training, deployment, data consistency, monitoring, governance, etc.)
We are looking for a person who has
- Strong coding skills Python/Scala or equivalent languages;
- Experimental design skills;
- Ability to interact and communicate effectively with business and engineering;
- Good understanding of model development and deployment pipeline;
- Experience with machine learning tools and libraries such as Scikit-learn, Mllib and etc;
- Nice data handling skills, Spark Dataframes, or SQL proficiency is a plus;
- English language proficiency.
Local Benefits
- Chance of earning equity at Nubank
- Extended maternity and paternity leaves
- Health and life insurance
- NuCare - Our mental health and wellness assistance program
- Nucleo - Our learning platform of courses
- NuLanguage - Our language learning program
- Gym partnership
Diversity & Inclusion
At Nu, we want to be sure that we're building a more diverse and inclusive workplace that reflects the customers we serve and seek to empower. That's why we hire based on equality. We consider gender, ethnicity, race, religion, sexual orientation, and other identity markers as enriching elements to our company while ensuring neither of them represent a barrier when recruiting fantastic talent.
Ready to apply?
Apply to Nubank