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

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
• 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 

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
• 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.

About 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. For more information, visit our institutional page https://international.nubank.com.br/careers/

Data Science at Nubank

Data Science is essential to every part of the business at Nubank, since Day 1. Most areas of the company use data science to some extent. Data Scientists are responsible for translating business needs into models and other types of data-driven solutions.

As a Data Scientist, you’re expected to

• Interact with other team members to understand how to best apply data science and artificial intelligence to Nubank’s problems;
• Investigate problems and discover insights through exploratory data analysis and other quantitative methods;
• Have great numerical and analytical literacy, including knowledge of statistics and linear algebra as needed;
• Work on multiple stages of the ML model lifecycle–from ideation to training and steady-state operation;
• Work across multiple types of models, algorithms and learning paradigms;
• Communicate technical points, decisions and tradeoffs to peers and stakeholders;
  
  

Our ideal candidate

• Has experience working with ML models in a production environment;
• Understands possible pitfalls and tradeoffs when applying ML to real-word, sometimes messy problems;
• Is familiar with the technical aspects of learning theory and algorithms and is able to discuss details if needed;
• Has experience working with the Python scientific stack (Numpy, Pandas, Scipy, Sklearn, Boosted Tree Libraries, etc);
• Has experience working with Tensor/Deep Learning libraries such as Tensorflow and Pytorch;
• Is able to connect analytical outcomes to business priorities and advise stakeholders on the best course of action;
• Has good communication skills and is able to convey technical topics and decision points to diverse audiences;
• Has experience writing general-purpose code using modern tools;
• Understands the implications of causality when applying ML to problems;
• Is up-to-date with recent developments in the area as a whole and is able to identify opportunities for improvement and innovation;
  
  

Our Benefits

• Chance of earning equity at Nubank
• Food/ Meal Card (Vale-Refeição and/or Vale Alimentação)
• Public Transportation Commuting Benefit (Vale-Transporte)
• NuCare – Psychological, Financial and Legal Assistance Program
• Life Insurance
• Medical Plan
• Dental Plan
• NuLanguage – Language Course Program
• Nucleo - Our learning platform of courses
• Extended Parental Leave
• Daycare Allowance
• Parental Consultancy
• Work-from-home Allowance
• Gym Partnerships
• 30 days of paid vacation
  
  

Work Model for this Role

Hybrid 2-3 times/week: Our hybrid work model brings us to the office at least twice a week, on strategic days designed to maximize team connection and collaboration. For more details, visit https://building.nubank.com/nu-hybrid-work-model/

A pessoa nessa posição é responsável por garantir a coleta, processamento, armazenamento e disponibilidade de dados de alta qualidade para impulsionar modelos de IA e fornecer insights acionáveis para clientes e stakeholders.

Trata-se de um papel de liderança técnica, que combina a execução de projetos complexos de ciência de dados com a mentoria do time e a coordenação de esforços de Pesquisa e Desenvolvimento (P&D) para promover a inovação.

Esse propósito se traduz, na prática, nos seguintes Jobs to Be Done:

Liderar a estratégia e execução de Ciência de Dados

• Liderar equipes em projetos complexos, orientando e mentorando membros do time;
• Propor e implementar estratégias para melhorar processos e práticas internas de ciência de dados;
• Decidir quando um modelo está pronto para produção e definir como monitorá-lo.

Desenvolver modelos de IA de ponta a ponta

• Desenvolver modelos utilizando técnicas de IA (estatística, machine learning e deep learning), desde a coleta e tratamento até a engenharia de features;
• Implementar algoritmos de aprendizado supervisionado e não supervisionado, otimizando-os para performance e eficiência;
• Encapsular modelos em APIs robustas, fáceis de usar e bem documentadas.

Transformar grandes volumes de dados em valor

• Coletar, processar e analisar grandes volumes de dados estruturados e não estruturados para identificar padrões e tendências;
• Criar visualizações que elucidem as decisões tomadas e a qualidade dos modelos desenvolvidos.

Atuar como ponte entre tecnologia e negócio

• Facilitar a comunicação entre diferentes departamentos e alavancar o potencial de equipes multidisciplinares;
• Interface direta com clientes e parceiros para levantamento de requisitos técnicos, especificação de arquitetura de modelos e apresentação de resultados.

Sustentar a cultura de inovação da Kunumi

• Manter-se atualizado com as últimas tendências e avanços em ciência de dados e IA;
• Atuar de forma colaborativa e alinhada ao "jeito Kunumi": autonomia, responsabilidade compartilhada e diálogo aberto.

O que é esperado de você

• Liderança Técnica: Experiência sólida liderando projetos e equipes técnicas.
• Domínio de Programação: Proficiência em Python (preferencial), R, SQL e bibliotecas como TensorFlow, PyTorch e Scikit-Learn.
• Ambiente de Desenvolvimento: Experiência de desenvolvimento em ambiente Linux.
• Experiência Prática: Aplicação real de métodos estatísticos e algoritmos de ML para resolver problemas complexos.
• Visualização de Dados: Experiência com ferramentas como Tableau, Matplotlib ou Seaborn.

O que pode ser um diferencial

• Big Data: Familiaridade com tecnologias de armazenamento e processamento como Hadoop, Spark e NoSQL.
• Pesquisa Aplicada: Experiência anterior coordenando esforços de P&D.
• Visão de Produto: Capacidade de traduzir limitações técnicas em soluções de negócio para clientes

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
• Generous holidays and flexible PTO
• 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.

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

A Capco é uma consultoria global de tecnologia e negócios, focada no setor de serviços financeiros e energia, somos apaixonados por ajudar nossos clientes, temos 40 escritórios nas Américas, Europa e Ásia-Pacífico! Se criatividade e inovação são sua paixão, a Capco é ideal para você. Vamos te apoiar e ajudar a acelerar sua carreira!

Estamos em busca de um(a) Machine Learning Engineer com foco em NLP e processamento de documentos para atuar no desenvolvimento de soluções inteligentes voltadas à automação e análise de dados, especialmente em contextos financeiros. O(a) profissional será responsável por construir pipelines de dados, desenvolver modelos de machine learning e implementar soluções escaláveis em ambiente cloud.

O que você fará:

• Desenvolver soluções de machine learning e NLP para extração e processamento de informações de documentos
• Trabalhar com OCR, parsing de PDFs e extração de dados de Excel
• Implementar algoritmos de fuzzy matching (ex: Levenshtein, record linkage)
• Construir e manter pipelines de dados e processos de ETL
• Desenvolver, treinar e validar modelos de machine learning (supervisionados e não supervisionados)
• Realizar deploy de modelos utilizando APIs REST, Docker e serviços Azure
• Integrar soluções com sistemas externos e aplicações corporativas
• Colaborar com times multidisciplinares para entrega de soluções escaláveis

O que você precisa ter:

• Forte experiência em Python (Pandas, NumPy, scikit-learn)
• Experiência com bibliotecas de NLP (spaCy, NLTK ou Transformers)
• Conhecimento em processamento de documentos (OCR, PDFs, Excel)
• Experiência com deploy de modelos (APIs REST, Docker, Azure ML)
• Entendimento de técnicas de machine learning supervisionado e não supervisionado
• Experiência com pipelines de dados e ETL
• 2 a 5 anos de experiência em Machine Learning, com foco em NLP ou dados financeiros
• Inglês avançado/fluente

Diferenciais

• Experiência no setor financeiro/fintech (reconciliação, contabilidade)
• Conhecimento em Azure Machine Learning e Cognitive Services
• Experiência com frameworks de deep learning (TensorFlow, PyTorch)
• Experiência com LLMs para entendimento de documentos (OpenAI, Azure OpenAI)

POR QUE CAPCO:

Na Capco promovemos uma cultura inclusiva. Valorizamos a diversidade em todas as suas expressões. 
Pensamos, em conjunto, sempre em ações diversas de inclusão e de responsabilidade social através de comitês internos geridos pela nossa comunidade interna, como os grupo de Mulheres, Pessoas Com Deficiência, Pessoas Negras, LGBTQIAPN+, Parentalidade, Gerações, entre outros. Nossas oportunidades são trabalhadas para todos(as)!
#LifeAtCapco

PRÓXIMOS PASSOS:

• Se você está ansioso(a) para progredir sua carreira conosco, candidate-se e aguarde o contato de um de nossos recrutadores!

About us

AB InBev is the leading global brewer and one of the world’s top 5 consumer product companies. With over 500 beer brands, we’re number one or two in many of the world’s top beer markets, including North America, Latin America, Europe, Asia, and Africa.

About AB InBev Growth Group

Created in 2022, the Growth Group unifies our business-to-business (B2B), direct-to-consumer (DTC), Sales & Distribution, and Marketing teams. By bringing together global tech and commercial functions, the Growth Group allows us to fully leverage data and drive digital transformation and organic growth for AB InBev around the world.

In addition to supporting well known global beer brands like Corona, Budweiser and Michelob Ultra, the Growth Group is home to a robust suite of digital products including our B2B digital commerce platform BEES, on-demand delivery services Ze Delivery and TaDa Delivery, and table top beer keg PerfectDraft.

We are an exceptional team, focused on understanding and supporting consumer and customer needs, harnessing new technology, and scaling growth opportunities.

What you'll do:

• Drive BEES AI’s mission, vision, and ML strategy into concrete technical roadmaps and align architecture and standards with long-term goals.
• Own technical end-to-end delivery of ML pipelines—from architecture and data/feature pipelines through model development, deployment, and production scaling—in partnership with data, platform, and product teams.
• Design and standardize the ML model development framework (project structure, experimentation, versioning, reproducibility) to improve consistency and reuse across teams.
• Build and implement observability across the model lifecycle (training and production) via monitoring, logging, and alerting for performance, quality, and drift.
• Ensure optimized deployments that minimize cost and maximize quality (e.g., scaling, resource allocation, inference optimization) while meeting SLAs.

What you'll need:

• Bachelor's degree in computer science, engineering, mathematics, or any quantitative field. A master's degree is a plus.
• Relevant experience with ML platform architecture (e.g., feature stores, model registries, training and inference pipelines).
• Systems thinking (scalability, reliability, latency, cost)
• Software design patterns for building maintainable, production-ready ML systems.
• Python, PySpark, and SQL. Java, Rust, and NodeJS are a plus
• Kubernetes, Databricks, Terraform, Azure Devops (Git), Azure Cloud, ML Frameworks (BentoML, Kedro, Seldon, KServe, Triton Inference Server, etc.) and ML Libraries (Scikit Learn, Pytorch, Tensor Flow, ONNX, etc.)

More about you:

• You´re motivated by building up world-class solutions that impact millions of users all over the world;
• You´re creative when solving problems and is continuously seeking improvements for processes and solutions;
• You can communicate clearly with your team and the company to identify issues and demands;
• You have the autonomy to recognize the current and new priorities, evaluating the impact of their outcomes in the final result.

What We Offer:

• Performance based bonus*
• Attendance Bonus* 
• Private pension plan
• Meal Allowance
• Casual office and dress code
• Days off*
• Health, dental, and life insurance
• Medicines discounts
• WellHub partnership
• Childcare subsidies
• Discounts on Ambev products*
• Clube Ben partnership
• Scholarship*
• School materials assurance
• Language and training platforms
• Transport allowance

*Rules applied

Equal Opportunity & Affirmative Action:

AB InBev Growth Group is proud to be an Equal Opportunity and Affirmative Action employer. We do not discriminate based upon of race, color, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other applicable legally protected characteristics.

The following fields are optional, but anticipate the information for your registration*.

Remember: your data will never be used as elimination criteria in selection processes. With them, AB InBev Growth Group is able to analyze diversity and reduce biases in selection processes. We want to contribute to changing this reality by being an inclusive company. 

For more information: www.abinbev.com 

Xometry is seeking a Senior Data Scientist to join our Generative AI team. The candidate will focus on training and fine-tuning Visual Language Models (VLMs) for multimodal document understanding. The ideal candidate will leverage their expertise in machine learning and computer vision to advance Xometry's capabilities in processing and extracting structured data from complex documents and images. This is a 1-year contract.  

Responsibilities:

• Develop, fine-tune, and evaluate Visual Language Models (VLMs) to enhance document understanding, focusing on multimodal data such as text, images, and technical drawings.
• Design and implement data preparation, cleaning, and augmentation processes tailored to multimodal model training, ensuring high-quality data pipelines for VLMs.
• Leverage transfer learning and pre-trained models to accelerate model development and optimize performance on Xometry’s specific data.
• Use cloud resources (e.g., Amazon Web Services) to scale training and fine-tuning processes for VLMs efficiently.
• Collaborate with data engineering and machine learning operations (MLOps) teams to deploy VLMs into production and monitor their performance.
• Interpret model outputs and improve model accuracy and robustness by applying data analysis and visualization tools (such as Python, Jupyter Notebooks, and SQL).
• Experiment with and implement state-of-the-art model architectures, continuously optimizing VLM performance in a fast-paced, iterative environment.
• Work within a team-oriented setting, participating in peer reviews, sharing insights, and contributing to an environment of continuous learning and improvement.

Qualifications:

• A bachelor’s degree is required; an advanced degree (M.S. or PhD) in computer science, data science, machine learning, or a related field is highly preferred.
• 5+ years of experience in data science and machine learning, with expertise in Visual Language Models or multimodal machine learning.
• Strong experience with machine learning libraries and frameworks such as PyTorch, TensorFlow, or Hugging Face.
• Proficiency in Python, including libraries like pandas, numpy, and scikit-learn.
• Solid understanding of deep learning techniques and experience with transfer learning, fine-tuning, and model evaluation.
• Experience with cloud platforms (e.g., AWS SageMaker) for model training and deployment.
• Familiarity with data processing and visualization tools (SQL, Jupyter Notebooks, Looker, etc.) and basic database knowledge (e.g., Snowflake, MongoDB).
• Excellent analytical and problem-solving skills, with a strong ability to work in an environment that values teamwork, innovation, and continuous learning.
• Familiarity with computer vision tasks and frameworks, as well as experience with multimodal data, is a plus.

#LI-Remote

Xometry is seeking a Senior Data Scientist to join our Data Science team. This person will drive data science initiatives, provide technical guidance when needed, and leverage data to solve complex business challenges. The ideal candidate has a deep understanding of data science methodologies, strong statistical expertise, and proven experience delivering impactful data solutions in a dynamic environment. This is a 1-year contract with the potential for extension.

Responsibilities:

• Use data analysis, visualization, and modeling tools (e.g., Looker, Jupyter Notebooks, Python, Pandas) to drive data-driven business strategies, with a focus on cost prediction for a two-sided marketplace.
• Design and execute data science projects, including hypothesis development, statistical analysis, and experimentation (e.g., A/B testing) to answer key business questions and validate assumptions.
• Collaborate with cross-functional teams to translate business needs into data science solutions and ensure alignment with Xometry’s objectives.
• Analyze existing algorithms for accuracy and performance, provide recommendations for optimization, and update production code as necessary.
• Query and extract data from databases (e.g., Snowflake, MongoDB) to support analysis and model development.
• Implement robust data science methodologies, emphasizing best practices in code reproducibility, version control, and model deployment.
• Continuously iterate and improve solutions within a fast-paced, agile environment, addressing challenges with innovative, data-driven approaches.
• Foster a culture of collaboration, teamwork, and continuous learning, encouraging knowledge sharing and skill development within the team.

Qualifications:

• A bachelor’s degree is required; an advanced degree (M.S. or PhD) in applied math, computer science, data science, engineering, or a related field is highly preferred.
• 5+ years of experience in data science
• Strong expertise in SQL and Python (numpy, scipy, pandas, and scikit-learn preferred) and experience with API integrations using Python.
• Proficient in applied statistics, including knowledge of probability distributions, confidence intervals, statistical tests (e.g., t-tests, ANOVA, Chi-Square), and power analysis.
• Experience with pricing algorithms and technology-driven product development.
• Knowledge of scientific software principles, including version control, code reproducibility, and deployment practices.
• Demonstrated ability to work in a fast-paced, ambiguous environment and manage shifting priorities effectively.
• Must be able to work core hours aligned with US Eastern Time (GMT-5).
• Strong communication and interpersonal skills to collaborate with global teams and articulate complex technical information to non-technical stakeholders.

#LI-Remote

Principais Atividades:

• Desenvolver e manter APIs e microservices com tecnologias modernas.
• Trabalhar com arquitetura de software, garantindo escalabilidade e segurança.
• Atuar com dados e analytics, utilizando ferramentas como SQL, Power BI, Python, Spark e plataformas de Big Data.
• Criar e otimizar soluções em Cloud Computing (AWS, Azure, GCP), com práticas DevOps e CI/CD.
• Participar de times ágeis (Scrum, Kanban) e colaborar em projetos inovadores para grandes empresas

Requisitos:

• 5-6 anos de experiência sólida experiência comprovada em desenvolvimento de software com foco em inteligência artificial;
• Graduação em Ciência da Computação, Engenharia, Matemática ou área relacionada
• Proficiência em Python e experiência com bibliotecas como TensorFlow, PyTorch, Scikit-learn, etc;
• Conhecimento em LLM (Linguagem de Modelagem Lógico) e RAG (Rede Adaptativa Generativa) é altamente desejável;
• Experiência em trabalhar com serviços em nuvem, especialmente AWS;
• Excelentes habilidades de resolução de problemas e capacidade de trabalhar de forma independente e em equipe;
  Inglês intermediário/avançado (possivel atuação com time internacional).

Por que a Capco:

Na Capco, promovemos uma cultura inclusiva. Valorizamos a diversidade em todas as suas expressões. Pensamos, em conjunto, sempre em ações diversas de inclusão e de responsabilidade social através de comitês internos geridos pela nossa comunidade, como os grupo de Mulheres, Pessoas Com Deficiência, Pessoas Negras, LGBTQIAPN+, Parentalidade, Gerações, entre outros. Juntar-se à Capco significa ingressar em uma organização comprometida com um ambiente de trabalho inclusivo onde você é incentivado a #BeYourselfAtWork (Ser Você Mesmo no Trabalho). Celebramos a individualidade e reconhecemos que a diversidade e a inclusão, em todas as formas, são fundamentais para o sucesso. Acreditamos que todos trazem algo diferente, por isso adoraríamos saber o que o torna diferente! #LifeAtCapco
Juntar-se à Capco significa ingressar em uma organização comprometida com um ambiente de trabalho inclusivo onde você é incentivado a #BeYourselfAtWork (Ser Você Mesmo no Trabalho). Celebramos a individualidade e reconhecemos que a diversidade e a inclusão, em todas as formas, são fundamentais para o sucesso. Acreditamos que todos trazem algo diferente, por isso adoraríamos saber o que o torna diferente!

Próximos Passos:

O objetivo da Capco é conduzir um processo flexível e alinhado às necessidades de cada oportunidade e talento.

• Etapa 1: Entrevista comportamental.
• Etapa 2: Entrevista técnica.
• Etapa 3: Conversa com time de Projeto/ Delivery.
• Etapa 4: Conversa com cliente.
• Etapa 5: Proposta ou feedback.

As etapas do nosso processo seletivo descritas são essenciais para garantir uma avaliação completa e assertiva. Contudo, elas podem ser ajustadas dependendo da senioridade do(a) candidato(a), da área de atuação e do formato do projeto. 

About the Role

As a Data Scientist, you will join the AI Models team at Weni by VTEX, a business unit of VTEX. You will be responsible for researching, developing, and improving machine learning models that power our conversational agents and multi-agent systems. You are driven by curiosity and impact, transforming complex data into tangible improvements in user experience and product performance.

What you will do

• Research, design, and implement machine learning models to improve the performance of conversational agents and multi-agent systems.
• Analyze large datasets to extract insights that guide model improvements and product decisions.
• Work closely with engineers and product managers to bring AI models from prototype to production. 
• Continuously evaluate model performance and iterate based on real-world usage and feedback.
• Perform exploratory data analysis (EDA) to uncover hidden structures, anomalies, and relationships in data, informing hypothesis generation and experimental design for conversational AI systems. 

Who you are

• Strong foundation in Statistics.
• Proficiency in Python.
• Experience using Git for code version control.
• Hands-on experience with scikit-learn, pandas, and other data-related frameworks.
• Knowledge of Deep Learning.
• Experience with cloud platforms (AWS) for storage, pipelines, and deployment.
• Monitoring performance, drift, and data quality in production.
• Critical thinking, curiosity, and proactive problem-solving.
• Clear communication (oral, written, and visual) and collaborative teamwork.
• Familiarity with GenAI/LLMs (prompt engineering, fine-tuning, hallucination evaluation).
• Bachelor’s degree in Computer Science, Engineering, Statistics, Mathematics, or related fields.

About VTEX

VTEX (NYSE: VTEX) is the composable and complete commerce platform that delivers more efficiency and less maintenance to organizations seeking to make smarter IT investments and modernize their tech stack. Through our pragmatic composability approach, we empower brands, distributors, and retailers with unparalleled flexibility and comprehensive solutions, enabling them to invest solely in what provides a clear business advantage and boosts profitability. VTEX is trusted by 2,400 global B2C and B2B customers, including Carrefour, Colgate, Motorola, Sony, Stanley Black & Decker, and Whirlpool, having 3,400 active online stores across 43 countries (as of FY ended on December 31, 2024).

Founded in the year 2000, VTEX has a history of being unstoppable. Completely against the odds, VTEX is leading a high-tech industry and positioned above market giants. We are building an extraordinary future with more than 1,300 employees scattered across 25 locations in 16 countries in Latin America, North America, Europe, and Asia. For more information, visit www.vtex.com.

At VTEX, you will work in a challenge-driven environment and collaborate with amazing peers. If you are powerful individually, join us, and we will be unstoppable together.

About Weni by VTEX

Weni by VTEX is a business unit of VTEX dedicated to enterprise customer experience solutions powered by artificial intelligence, designed for brands and retailers.

With the Weni Platform, we redefine sales and post-sales journeys through hyperautomation and the smart integration of data into conversational channels, delivering faster, more personalized, and more efficient experiences for consumers.

Benefits

• Health, dental, and life insurance with national coverage provided by VTEX;
• Language development incentive program (English, Spanish, Portuguese);
• Flexible meal allowance;
• Extended parental leaves;
• Flexible work schedule and remote-first culture;
• Financial assistance to build your work-from-home setup;
• Wellness program;
• Budget for Development.