MLOps Jobs in Chile.
All active MLOps roles based in Chile.
Pick a job to read the details
Tap any role on the left — its description and apply link will open here.
The salary range for this role is $50,000 - $120,000 per year (Gross in USD)
About Sezzle:
With a mission to financially empower the next generation, Sezzle is revolutionizing the shopping experience beyond payments, blending cutting-edge tech with seamless, interest-free installment plans that make shopping smarter and more accessible. We’re not just transforming payments; we’re redefining how people discover, interact with, and purchase the things they love while driving real impact on merchant sales through increased conversions and higher order values. As we continue to shape the future of fintech and retail, we’re building an innovative, dynamic team passionate about creating more than just a transaction but a truly unique shopping journey. If you’re excited about pushing boundaries in tech and delivering a game-changing experience for consumers and merchants alike, come join us at Sezzle and help create the future of shopping!
About the Role:
We are seeking a talented and motivated Sr. AI engineer who is best in class with a high IQ plus a high EQ. This role presents an exciting opportunity to thrive in a dynamic, fast-paced environment within a rapidly growing team, with abundant prospects for career advancement.
You will be responsible for overseeing the design, development, and deployment of machine learning models that power and enhance our financial platform. In this role, you will drive the creation of scalable machine learning solutions for personalized recommendations in the Sezzle marketplace, fraud detection, and credit risk assessment, utilizing a combination of cloud services, open-source tools, and proprietary algorithms.
Your leadership will be key in blending machine learning development and operations (MLOps) to automate and optimize the full lifecycle of our ML models. You will collaborate with a team of engineers and data scientists to build large-scale, high-quality solutions that address diverse challenges in the shopping and fintech space. You’ll ensure our AI-driven features are robust, efficient, and scalable as we continue to grow.
What You'll Do:
- Design, Build, and Maintain Scalable ML Infrastructure: Lead the design and development of scalable machine learning infrastructure on AWS, utilizing services like AWS Sagemaker for efficient model training and deployment.
- Collaborate with Product Teams: Work closely with product teams to develop MVPs for AI-driven features, ensuring quick iterations and market testing to refine solutions effectively.
- Develop Monitoring & Alerting Frameworks: Create and enhance monitoring and alerting systems for machine learning models to ensure high performance, reliability, and minimal downtime.
- Support Marketing Team’s AI Utilization with an eye on Cross-Departmental impact: Enable various departments within the organization to leverage AI/ML models, including cutting-edge Generative AI solutions, for different use cases.
- Provide Production Support: Offer expertise in debugging and resolving issues related to machine learning models in production, participating in on-call rotations for operational troubleshooting and incident resolution.
- Scale ML Architecture: Design and scale machine learning architecture to support rapid user growth, leveraging deep knowledge of AWS and ML best practices to ensure robustness and efficiency.
- Mentor and Elevate Team Skills: Conduct code reviews, mentor team members, and elevate overall team capabilities through knowledge sharing and collaboration.
- Stay Ahead of the Curve: Stay updated with the latest advancements in machine learning technologies and AWS services, driving the adoption of cutting-edge solutions to maintain a competitive edge.
What We Look For:
- Bachelor's degree in Computer Science, Computer Engineering, Machine Learning, Statistics, Physics, or a relevant technical field, or equivalent practical experience.
- Demonstrated experience working with Claude or equivalent large language model tools is required; candidates must be comfortable leveraging AI to enhance productivity, research, and communication.
- At least 6+ years of experience in machine learning engineering, with demonstrated success in deploying scalable ML models in a production environment.
Preferred Qualifications:
- Deep expertise in one or more of the following areas: machine learning, recommendation systems, pattern recognition, data mining, artificial intelligence, or related technical fields.
- Proven track record of developing machine learning models from inception to business impact, demonstrating the ability to solve complex challenges with innovative solutions.
- Proficiency with Python is required, and experience with Golang is a plus.
- Demonstrated technical leadership in guiding teams, owning end-to-end projects, and setting the technical direction to achieve project goals efficiently.
- Experience working with relational databases, data warehouses, and using SQL to explore them.
- Strong familiarity with AWS cloud services, especially in deploying and managing machine learning solutions and scaling them in a cost-effective manner.
- Knowledgeable in Kubernetes, Docker, and CI/CD pipelines for efficient deployment and management of ML models.
- Comfortable with monitoring and observability tools tailored for machine learning models (e.g., Prometheus, Grafana, AWS CloudWatch) and experienced in developing recommender systems or enhancing user experiences through personalized recommendations.
- Solid foundation in data processing and pipeline frameworks (e.g., Apache Spark, Kafka) for handling real-time data streams.
About You:
- You have relentlessly high standards - many people may think your standards are unreasonably high. You are continually raising the bar and driving those around you to deliver great results. You make sure that defects do not get sent down the line and that problems are fixed so they stay fixed.
- You’re not bound by convention - your success—and much of the fun—lies in developing new ways to do things
- You need action - speed matters in business. Many decisions and actions are reversible and do not need extensive study. We value calculated risk-taking.
- You earn trust - you listen attentively, speak candidly, and treat others respectfully.
- You have backbone; disagree, then commit - you can respectfully challenge decisions when you disagree, even when doing so is uncomfortable or exhausting. You have conviction and are tenacious. You do not compromise for the sake of social cohesion. Once a decision is determined, you commit wholly.
- You deliver results - you focus on the key inputs and deliver them with the right quality and in a timely fashion. Despite setbacks, you rise to the occasion and never settle.
What Makes Working at Sezzle Awesome:
At Sezzle, we are more than just brilliant engineers, passionate data enthusiasts, out-of-the-box thinkers, and determined innovators. We believe in surrounding ourselves with only the best and the brightest individuals. Our culture is not defined by a certain set of perks designed to give the illusion of the traditional startup culture, but rather, it is the visible example living in every employee that we hire.
#Li-remote #full-time
Ready to apply?
Apply to SezzleSenior Machine Learning Engineer
Share this job
Senior Machine Learning Engineers at Thoughtworks build, maintain and test the architecture and infrastructure for managing machine learning applications. They are involved in supporting and contributing to the design of the end-to-end applications and products. They are responsible for building core capabilities including technical and functional machine learning systems and applications, being the anchor for functional streams of work and are accountable for timely delivery.
As a senior machine learning engineer, you will work on the latest tools, frameworks and offerings while also being involved in enabling credible and collaborative problem solving to execute on a strategy.
Job responsibilities
- You will contribute to design and drive the development of robust scalable architectures and infrastructure for deploying and managing machine learning (ML) applications, ensuring high availability, performance and security.
- You will collaborate with data scientists and engineers to translate business needs into effective and efficient ML systems and applications.
- You will own the development and maintenance of core functionalities within ML applications, including ML pipelines, model training and deployment, and monitoring and evaluation.
- You will drive the functional stream of work by providing technical expertise, handling team discussions and ensuring timely delivery of assigned tasks.
- You will stay ahead of the curve by actively exploring and implementing the latest tools, frameworks and offerings in the ML landscape.
- You will facilitate collaborative problem solving within the team by actively listening, communicating effectively and mentoring other engineers.
- You will contribute to the development and execution of the team's overall ML strategy, aligning technical capabilities with business objectives.
- You will proactively identify and address challenges related to ML systems and applications, proposing solutions and implementing improvements.
Job qualifications
Technical Skills
- An advanced English level is required.
- You must have a strong experience with LLM and AI.
- You have experience in writing clean, maintainable and testable code, demonstrating attention to refactoring and readability of the code.
- You are proficient in scripting languages such as Python or Shell for automation and task streamlining.
- You have knowledge of distributed systems and scalable architectures to handle large-scale ML applications.
- You have experience with building, deploying, and maintaining ML systems using relevant ML techniques and platforms, i.e.: Scikit-learn, Tensorflow, MLFlow, Kubeflow, Pytorch.
- You have experience with building, deploying and maintaining ML systems and experience with application of MLOps principles and CI/CD to ML.
- You have experience in machine learning engineering and data science, are familiar with key ML concepts, algorithms and frameworks, and understand ML model lifecycles.
- You have experience with designing and operating the infrastructure required to run different types of ML training and serving workloads, i.e.: on-premise vs. cloud infrastructure, infrastructure as code, monitoring, etc.
- You have hands-on experience with on-premise and cloud services for building and deploying ML pipelines, i.e.: Azure, AWS, GCP or Databricks and associated ML managed services.
Professional Skills
- You understand the importance of stakeholder management and can easily liaise between clients and other key stakeholders throughout projects, ensuring buy-in and gaining trust along the way.
- You are resilient in ambiguous situations and can adapt your role to approach challenges from multiple perspectives.
- You don’t shy away from risks or conflicts, instead you take them on and skillfully manage them.
- You are eager to coach, mentor and motivate others and you aspire to influence teammates to take positive action and accountability for their work.
- You enjoy influencing others and always advocate for technical excellence while being open to change when needed.
Other things to know
Learning & Development
There is no one-size-fits-all career path at Thoughtworks: however you want to develop your career is entirely up to you. But we also balance autonomy with the strength of our cultivation culture. This means your career is supported by interactive tools, numerous development programs and teammates who want to help you grow. We see value in helping each other be our best and that extends to empowering our employees in their career journeys.
About Thoughtworks
Thoughtworks is a dynamic and inclusive community of bright and supportive colleagues who are revolutionizing tech. As a leading technology consultancy, we’re pushing boundaries through our purposeful and impactful work. For 30+ years, we’ve delivered extraordinary impact together with our clients by helping them solve complex business problems with technology as the differentiator. Bring your brilliant expertise and commitment for continuous learning to Thoughtworks. Together, let’s be extraordinary.
#LI-Remote
Ready to apply?
Apply to Referrals Only
DevOps Engineer
Share this job
Linqia is the leader in the influencer marketing industry. We’re a growing tech start-up, having experienced 100% year-over-year growth and break-even. At Linqia, we partner with the world’s largest brands including Danone, AB InBev, Kimberly-Clark, Unilever, and Walmart to build compelling and effective influencer marketing campaigns. Our AI-driven platform and team of experts are leading the transformation of influencer marketing.
We value intelligence, recognize talent and have instilled a culture that supports career development and growth for our employees. We thrive on innovation and accountability, with a customer first attitude that adds true value to everything we touch. Our team members are smart, hard-working, have integrity and love to have fun as we play to win.
SOFTWARE AND INFRASTRUCTURE DEVOPS ENGINEER
Experience level 3+ YoE supporting production saas in AWS
Location Anywhere in LATAM preferably in Colombia
Employment type Full‑time contract
ABOUT THE ROLE
Build out a cloud‑native team that owns the entire software delivery life cycle on Amazon Web Services. You will combine deep Kubernetes expertise with Python and shell scripting to automate, monitor, and continuously improve the Linqia platform while driving FinOps practices to keep our cloud footprint efficient. Work in a GitOps culture where every change is delivered through pull requests and rolled out by automated pipelines.
WHAT YOU WILL DO
- Design, maintain, and evolve our AWS account structure, VPC networking, IAM policies, security boundaries, and cost‑management controls using Terraform and the AWS console
- Maintain secure networking layers with AWS load balancers, ingress controllers, service‑mesh policies, network policies, and zero‑trust principles
- Operate and harden production‑grade Kubernetes clusters on AWS EKS, including upgrades, service mesh, policy management, and multi‑cluster architectures driven by Argo CD
- Build reusable infrastructure‑as‑code modules with Terraform that provision cloud resources in minutes while enforcing tagging standards and least‑privilege access
- Create self‑service CI/CD pipelines in Jenkins and GitHub Actions for fast, safe releases with automated testing and promotion across environments
- Deliver real‑time observability with Datadog, Prometheus, Grafana, CloudWatch, and OpenTelemetry, and use these tools to assist in solving production bugs and issues
- Administer and maintain purpose-built Linux VMs via configuration management tools like Puppet, Ansible, or Chef
- Deploy, scale, and maintain databases on AWS (Aurora, PostgreSQL, MySQL, OpenSearch, etc.), maintaining high database performance/uptime, optimizing tables and datasets, and ensuring disaster recovery protocols are in place.
- Support developers by maintaining Podman‑based local dev boxes and Kubernetes staging environments that mirror production, ensuring smooth hand‑off from local code to cloud‑native deployments
- Implement FinOps practices: track and forecast AWS spend, enforce cost‑allocation tagging, identify rightsizing opportunities, manage Savings Plans or Reserved Instances, and build cost‑optimisation dashboards for engineering and finance stakeholders
- Write automation utilities and command‑line tools in Python and craft shell scripts that glue components and workflows together
- Champion reliability through incident reviews, capacity planning, game days, chaos testing, and service‑level objective tracking
- Collaborate in Agile rituals, plan sprints, refine backlog tickets, and pair with peers to spread DevOps and FinOps best practices
WHAT YOU BRING
- Bachelor degree in Computer Science or equivalent practical experience
- three plus years working with cloud infrastructure or platform engineering focused on AWS
- Deep hands‑on experience with Kubernetes, preferably EKS, covering upgrades, networking, storage, RBAC, and custom resources
- Proficiency in Python and Bash or Zsh scripting
- Strong understanding of core AWS services EC2, VPC, IAM, ALB, S3, RDS, CloudFormation, and CloudWatch
- Demonstrated experience applying FinOps principles: cost monitoring, forecasting, and optimisation on AWS
- Solid experience with Docker and container runtimes, with emphasis on Podman for local development environments
- Hands‑on practice with configuration‑management tools such as Ansible or Puppet and infrastructure‑as‑code with Terraform
- Proven use of Datadog for metrics, logs, and APM, plus familiarity with Prometheus and Grafana dashboards
- Comfortable with Git‑based workflows, feature branching, and pull‑request reviews
- Strong SQL skills and a deep understanding of relational database internals
- Competent in Linux administration, process troubleshooting, and performance tuning
- Practical knowledge of TCP/IP, HTTP, TLS, DNS, and common networking tools
- Clear communication skills and an ability to translate complex technical topics to diverse audiences
- Familiarity with Scrum or Kanban and a continuous‑improvement mindset
EXTRA CREDIT
- AWS certifications such as Solutions Architect, DevOps Engineer, or FinOps Practitioner
- Experience with AWS security tooling GuardDuty, Security Hub, IAM Access Analyzer, and KMS
- Building data pipelines with Apache Spark, Flink, or similar frameworks
- Implementing event‑driven architectures with Kafka Streams or KSQL
- Applying SRE practices such as error budgets and service‑level dashboards
- Exposure to machine‑learning workflows, ModelOps, or MLOps in production
This position description is intended to describe the duties most frequently performed by an individual in this position. Linqia is an equal opportunity employer supporting workforce diversity. We actively encourage women, people with disabilities, veterans, underrepresented minorities, and LGBTQ+ people to apply. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity or expression, sexual orientation, age, marital status, veteran status, or disability status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Headhunters and recruitment agencies may not submit resumes or candidates through this Web site or directly to employees. Linqia does not accept unsolicited headhunter and agency resumes and will not pay fees to any third-party agency or company that does not have a signed agreement with us.
Ready to apply?
Apply to Linqia
DevOps Engineer
Share this job
Linqia is the leader in the influencer marketing industry. We’re a growing tech start-up, having experienced 100% year-over-year growth and break-even. At Linqia, we partner with the world’s largest brands including Danone, AB InBev, Kimberly-Clark, Unilever, and Walmart to build compelling and effective influencer marketing campaigns. Our AI-driven platform and team of experts are leading the transformation of influencer marketing.
We value intelligence, recognize talent and have instilled a culture that supports career development and growth for our employees. We thrive on innovation and accountability, with a customer first attitude that adds true value to everything we touch. Our team members are smart, hard-working, have integrity and love to have fun as we play to win.
SOFTWARE AND INFRASTRUCTURE DEVOPS ENGINEER
Experience level 3+ YoE supporting production saas in AWS
Location Anywhere in LATAM preferably in Colombia
Employment type Full‑time contract
ABOUT THE ROLE
Build out a cloud‑native team that owns the entire software delivery life cycle on Amazon Web Services. You will combine deep Kubernetes expertise with Python and shell scripting to automate, monitor, and continuously improve the Linqia platform while driving FinOps practices to keep our cloud footprint efficient. Work in a GitOps culture where every change is delivered through pull requests and rolled out by automated pipelines.
WHAT YOU WILL DO
- Design, maintain, and evolve our AWS account structure, VPC networking, IAM policies, security boundaries, and cost‑management controls using Terraform and the AWS console
- Maintain secure networking layers with AWS load balancers, ingress controllers, service‑mesh policies, network policies, and zero‑trust principles
- Operate and harden production‑grade Kubernetes clusters on AWS EKS, including upgrades, service mesh, policy management, and multi‑cluster architectures driven by Argo CD
- Build reusable infrastructure‑as‑code modules with Terraform that provision cloud resources in minutes while enforcing tagging standards and least‑privilege access
- Create self‑service CI/CD pipelines in Jenkins and GitHub Actions for fast, safe releases with automated testing and promotion across environments
- Deliver real‑time observability with Datadog, Prometheus, Grafana, CloudWatch, and OpenTelemetry, and use these tools to assist in solving production bugs and issues
- Administer and maintain purpose-built Linux VMs via configuration management tools like Puppet, Ansible, or Chef
- Deploy, scale, and maintain databases on AWS (Aurora, PostgreSQL, MySQL, OpenSearch, etc.), maintaining high database performance/uptime, optimizing tables and datasets, and ensuring disaster recovery protocols are in place.
- Support developers by maintaining Podman‑based local dev boxes and Kubernetes staging environments that mirror production, ensuring smooth hand‑off from local code to cloud‑native deployments
- Implement FinOps practices: track and forecast AWS spend, enforce cost‑allocation tagging, identify rightsizing opportunities, manage Savings Plans or Reserved Instances, and build cost‑optimisation dashboards for engineering and finance stakeholders
- Write automation utilities and command‑line tools in Python and craft shell scripts that glue components and workflows together
- Champion reliability through incident reviews, capacity planning, game days, chaos testing, and service‑level objective tracking
- Collaborate in Agile rituals, plan sprints, refine backlog tickets, and pair with peers to spread DevOps and FinOps best practices
WHAT YOU BRING
- Bachelor degree in Computer Science or equivalent practical experience
- three plus years working with cloud infrastructure or platform engineering focused on AWS
- Deep hands‑on experience with Kubernetes, preferably EKS, covering upgrades, networking, storage, RBAC, and custom resources
- Proficiency in Python and Bash or Zsh scripting
- Strong understanding of core AWS services EC2, VPC, IAM, ALB, S3, RDS, CloudFormation, and CloudWatch
- Demonstrated experience applying FinOps principles: cost monitoring, forecasting, and optimisation on AWS
- Solid experience with Docker and container runtimes, with emphasis on Podman for local development environments
- Hands‑on practice with configuration‑management tools such as Ansible or Puppet and infrastructure‑as‑code with Terraform
- Proven use of Datadog for metrics, logs, and APM, plus familiarity with Prometheus and Grafana dashboards
- Comfortable with Git‑based workflows, feature branching, and pull‑request reviews
- Strong SQL skills and a deep understanding of relational database internals
- Competent in Linux administration, process troubleshooting, and performance tuning
- Practical knowledge of TCP/IP, HTTP, TLS, DNS, and common networking tools
- Clear communication skills and an ability to translate complex technical topics to diverse audiences
- Familiarity with Scrum or Kanban and a continuous‑improvement mindset
EXTRA CREDIT
- AWS certifications such as Solutions Architect, DevOps Engineer, or FinOps Practitioner
- Experience with AWS security tooling GuardDuty, Security Hub, IAM Access Analyzer, and KMS
- Building data pipelines with Apache Spark, Flink, or similar frameworks
- Implementing event‑driven architectures with Kafka Streams or KSQL
- Applying SRE practices such as error budgets and service‑level dashboards
- Exposure to machine‑learning workflows, ModelOps, or MLOps in production
This position description is intended to describe the duties most frequently performed by an individual in this position. Linqia is an equal opportunity employer supporting workforce diversity. We actively encourage women, people with disabilities, veterans, underrepresented minorities, and LGBTQ+ people to apply. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity or expression, sexual orientation, age, marital status, veteran status, or disability status. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Headhunters and recruitment agencies may not submit resumes or candidates through this Web site or directly to employees. Linqia does not accept unsolicited headhunter and agency resumes and will not pay fees to any third-party agency or company that does not have a signed agreement with us.
Ready to apply?
Apply to Linqia
Whoa — hold up
JobsRadar was built for real people having a rough time in their job search — not for automated requests. You're clicking way too fast and you're now temporarily blocked.
Come back later. If you're genuinely job hunting, we've got your back — just act like a human.