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.
• 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

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.}

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

• Pay in USD
• 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

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 

• Pay in USD
• 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.

The Mission

We are seeking an Engineering Manager for our Cloud Data Engineering Practice. The right candidate is someone who is technically strong, can manage a team of Architects and Engineers, oversee data delivery engagements and is passionate about working with our customers, partners and employees to drive the business forward.

Your Assignment

• Manage a team of 5 - 8 cloud engineers and architects
• Interface with clients and internal teams to propose technical improvements using your cloud and engineering knowledge
• Take on hands-on technical responsibilities, including technical tasks in sprints 
• Lead technical discussions with customers
• Handle code reviews and pull request management
• Manage task assignments within sprints
• Perform technical leadership activities like roadmap and vision checks, best practices implementation, and risk identification
• In-project mentoring (understand people on project & creation of growth opportunities) and sharing feedback for team members on the project
• Technical accountability on the delivery
• Kickoff new client engagements and act as a point of technical escalation
• Develop and review proposals and contracts and present these to clients
• Perform technical interviews for Architect and Engineer candidates
• Perform weekly 1:1s with your direct reports and guide their career development
•  

Your Qualifications

• Strong experience in AWS data landscape
• Expertise in at least two major areas of Cloud Data Engineering:
  • Relational database design, optimization and migration
  • Big data processing using Spark, Hadoop, EMR etc
  • Machine Learning and MLOps
  • Advanced analytics using time series forecasting, descriptive and inferential statistics
  • BI dashboards and visualizations
  • Data Governance and MDM
• Experience with most of these:
  • Infrastructure as Code tools
  • CI/CD pipelines
  • Application modernization and migration with a strong focus on databases
• Excellent written and verbal communication skills

Benefits

• Pay in USD
• 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

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.

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 

• Pay in USD
• 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. 

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

The Role

The Data Practice Director is a senior leadership role within Caylent's Cloud Data Engineering (CDE) practice, reporting directly to the Data Practice Leader. This person is responsible for operationalizing the practice strategy, leading a team of data professionals, and owning delivery excellence across a portfolio of client engagements. They are the connective tissue between strategic direction and day-to-day execution, ensuring the practice delivers consistent, high-quality outcomes while scaling to meet growth targets.

Your Assignment

Practice Operations and Delivery

• Own end-to-end delivery oversight for a portfolio of data engineering and analytics engagements, ensuring on-time, on-budget, and high-quality outcomes.
• Drive operational rigor across the practice including utilization management, staffing coordination, margin tracking, and engagement health reviews.
• Develop and enforce delivery standards, reusable frameworks, and best practices that enable consistent, scalable execution across the team.
• Serve as an escalation point for active engagements, helping resolve technical blockers, scope issues, and client concerns in partnership with delivery leads.
• Partner with the Practice Leader to translate the strategic roadmap into executable quarterly priorities and team-level OKRs.

Team Leadership and Development

• Directly lead and develop a team of data engineers, architects, and analytics professionals across the practice.
• Set clear performance expectations, conduct regular reviews, and provide ongoing coaching to build a high-performing, engaged team.
• Partner with recruiting and workforce planning to identify talent gaps, support hiring decisions, and onboard new team members effectively.
• Foster a culture of ownership, continuous learning, and technical excellence across a globally distributed team.
• Identify and develop emerging leaders within the practice, creating growth pathways aligned to practice needs.

Client Engagement

• Build trusted advisor relationships with key client stakeholders, acting as a senior point of contact for delivery and strategic guidance.
• Partner with the sales and GTM teams to support business development activities including scoping, proposal development, and technical solutioning for new and existing accounts.
• Identify and pursue expansion opportunities within active client engagements, surfacing new use cases and service offerings that align with client needs.
• Represent Caylent with credibility and professionalism in client-facing conversations at the director and VP level.

Practice Growth and Enablement

• Collaborate with the Practice Leader on go-to-market positioning, service offering development, and internal capability building.
• Contribute to thought leadership content including whitepapers, webinars, and conference presentations that elevate the practice's external profile.
• Stay current on trends in the modern data ecosystem and work with the team to incorporate relevant innovations into our delivery approach.
• Support the development of practice IP including accelerators, reference architectures, and reusable solution components.

Your Qualifications

Experience

• 12+ years of experience in professional services or consulting, with a strong focus on data engineering, analytics, or data platform delivery.
• Demonstrated success managing and growing a team of 20 or more data professionals in a services or consulting environment.
• Proven track record of delivering complex data projects on AWS, including data lake, lakehouse, streaming, and analytics workloads.
• Experience managing a portfolio of concurrent client engagements, balancing delivery quality with financial performance.
• Hands-on background in data architecture or engineering, with the ability to engage credibly with technical teams and client stakeholders alike.

Leadership

• Strong people management skills with demonstrated ability to develop talent, drive performance, and build team culture across geographies.
• Ability to operate effectively at multiple levels, from strategic planning conversations with the Practice Leader to hands-on engagement support with delivery teams.
• Comfortable navigating ambiguity and complexity in a fast-paced, high-growth consulting environment.
• Experience influencing senior client stakeholders (director, VP and C-level) in delivery and advisory contexts.

Technical Acumen

• Deep familiarity with the AWS data ecosystem including services such as Redshift, Glue, EMR, Kinesis, Lake Formation, and Athena.
• Working knowledge of adjacent platforms including Databricks and Snowflake, and their integration with AWS-native architectures.
• Broad understanding of data engineering fundamentals: ingestion patterns, transformation frameworks (e.g., Spark, dbt), data modeling, governance, and quality management.
• Familiarity with MLOps and data science workflows sufficient to support and position those capabilities in client conversations.

Education

• Bachelor's degree in Computer Science, Data Science, Information Systems, or a related field.
• Master's degree or MBA is a plus.

Certifications

• AWS Certified Data Analytics, AWS Certified Solutions Architect, or equivalent certifications are a plus.

Preferred Qualifications

• Execution-Oriented: Skilled at translating strategy into structured plans and holding teams accountable to outcomes.
• Client-First Mindset: Genuinely invested in client success and capable of building durable, trust-based relationships.
• Collaborative Partner: Works effectively across sales, delivery, and practice leadership functions without siloed thinking.
• Clear Communicator: Able to convey complex technical and business concepts to diverse audiences with clarity and confidence.
• Growth Driver: Passionate about building teams, capabilities, and client relationships that compound over time.

Benefits

• Pay in USD
• 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

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.}

Santex is a US-based global company founded in 1999, with 26 years of experience in the software industry. Headquartered in California with offices in Córdoba, Argentina, its talent network spans over 18 countries thanks to its flexible, remote-first culture. Santex specializes in custom enterprise software development, operating through Hubs that include eCommerce, BIM, Mobility, Content Delivery, Integration, Web & Mobile Development, Cloud Computing, Artificial Intelligence (AI), Data Science, IT Consulting, and Services. The company is committed to making a positive impact across three dimensions: economic, social, and environmental.

Job Description: Machine Learning Engineer

We are looking for an experienced and driven Machine Learning Engineer to join our Advanced Analytics team. You will play a pivotal role in advancing our machine learning capabilities, focusing on the building, training, deploying, scoring, and monitoring of models for various use cases, including personalized recommenders, forecasting, and LLM modeling.

Responsibilities

• Model Development & Deployment: Develop and deploy Machine Learning, Deep Learning, and GenAI models to enhance operational efficiency and customer experience.
• Continuous Improvement: Improve models by monitoring performance, conducting A/B testing, and implementing feedback loops.
• Architecture & Scalability: Architect and rebuild complex ML frameworks from the ground up, incorporating multi-threaded processing and distributed workloads to support scalable pipelines.
• Cross-functional Collaboration: Work with engineers and product managers to integrate AI solutions into production systems within a fast-paced Agile environment.
• Operational Excellence: Own production ML systems, participating in on-call rotations, troubleshooting incidents, and maintaining overall model reliability.
• Innovation: Stay updated with the latest advancements in AI to ensure our solutions remain cutting-edge.

Requirements

• Education: Bachelor’s or advanced degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field.
• Experience: 3+ years of experience designing, building, and operating production-scale machine learning systems.
• Technical Mastery: Expert-level programming in Python with extensive experience using PySpark and distributed data platforms (e.g., Databricks) for large-scale datasets.
• Deep Learning & Frameworks: Strong experience with Scikit-Learn, MLlib, and PyTorch, including regression, time series, clustering, and deep learning.
• Distributed Systems: Deep understanding of Spark execution models, partitioning strategies, shuffle optimization, and vectorized processing (Pandas UDFs).
• Data & Cloud: Strong SQL expertise and experience deploying ML workloads to AWS (EC2, S3, DynamoDB, Lambda).
• Ops: Experience implementing MLOps / LLMOps practices.

Key Competencies

• Ownership: Ability to manage strategic initiatives in a rapidly evolving QSR environment.
• Adaptability: Eagerness to learn and adapt in ambiguous problem spaces with a collaborative attitude.
• Outcome-Oriented: Focus on analyzing and visualizing data to drive continuous improvement across the business.
• Professionalism: Maintain transparency and professionalism in communication within the team and with stakeholders.

📍 Location

• Flexible / Remote options available.

Who we are

At Domino, we build software that helps the largest, AI-driven organizations build and operate advanced data science and AI solutions at scale. Our platform integrates a streamlined model development environment, MLOps capabilities, and novel features for collaboration, reuse, and reproducibility — all of which make data science teams more productive, reduce time to value, and ensure compliance. Our customers — like Johnson & Johnson, GSK, Bristol Myers, UBS, FINRA and the US Navy — are using our software to solve some of the most important challenges in the world, such as developing new medicines, securing our financial markets, or protecting our country. Backed by Sequoia Capital, Coatue Management, NVIDIA, Snowflake and other leading investors, we have been in business for a decade but are still a small team operating with the spirit of a startup. Especially in the world of AI today, we believe that the future is still being invented — and we want to be the ones building it. For more information, visit www.domino.ai

What we are building

The Model Development Lifecycle Team is building a cutting-edge platform to simplify the entire machine learning journey. From development and training to deployment and management, we empower teams to turn data into actionable insights. Our platform supports:

• Seamless API Integration: Deploy models as APIs for consistent use across applications, whether on-premises, in enterprise infrastructures, or through third-party hosting.
• Collaboration and Discoverability: Use our model registry to version, store, and easily find models across the organization.
• Scalable Training Resources: Leverage advanced tools like GPUs, Ray, and Spark to meet the needs of diverse AI projects.

By supporting organizations in developing, registering, and scaling AI models, we enable impactful insights and innovation across the enterprise.

What your impact will be

In your first year, you will:

• Integrate model monitoring to provide a holistic view of deployment health and performance
• Enhance tagging capabilities across Domino entities to improve discoverability and tracking
• Expand LLM hosting capabilities to address customer needs for scale, performance, and logging
• Innovate within our Domino Apps offering by incorporating feature requests from major customers

What we look for in this role

• Building Scalable Systems: Hands-on experience developing and managing high-performance back-end systems in distributed computing environments
• Collaboration Across Teams: Working closely with cross-functional teams to integrate systems with front-end interfaces and third-party services
• API Development: Designing and implementing secure, scalable APIs (e.g., RESTful APIs, gRPC)
• Performance Optimization: Profiling and optimizing back-end performance, especially in cloud environments or with container technologies like Docker and Kubernetes.
• Testing and CI/CD: Using robust testing frameworks (unit, integration, end-to-end) and setting up CI/CD pipelines
• ML Model Deployment: Familiarity with model registries, versioning, and lifecycle management tools like MLflow or KubeFlow is a big plus
• Distributed Computing: Experience with frameworks like Apache Spark, Azure ML, or SageMaker is a plus
• Cloud Platforms: Proficiency with cloud providers (AWS, Azure, GCP) and deploying services in these environments
• Back-End Development: Expertise in languages such as Python, Java, Scala, or Go

What we value

• We value a growth mindset. High-performing creative individuals who dig into problems and see the opportunities for success
• We believe in individuals who seek truth and speak the truth and can be their whole selves at work
• We value all of you that believe improving is always possible At Domino Everything is a work in progress – we can do better at everything
• We emphasize an environment of teaching and learning to equip employees with the tools needed to be successful in their function and the company
• We strongly believe in the value of growing a diverse team and encourage people of all backgrounds, genders, ethnicities, abilities, and sexual orientations to apply

#LI-Remote

Who we are

At Domino, we build software that helps the largest, AI-driven organizations build and operate advanced data science and AI solutions at scale. Our platform integrates a streamlined model development environment, MLOps capabilities, and novel features for collaboration, reuse, and reproducibility — all of which make data science teams more productive, reduce time to value, and ensure compliance. Our customers — like Johnson & Johnson, GSK, Bristol Myers, UBS, FINRA and the US Navy — are using our software to solve some of the most important challenges in the world, such as developing new medicines, securing our financial markets, or protecting our country. Backed by Sequoia Capital, Coatue Management, NVIDIA, Snowflake and other leading investors, we have been in business for a decade but are still a small team operating with the spirit of a startup. Especially in the world of AI today, we believe that the future is still being invented — and we want to be the ones building it. For more information, visit www.domino.ai

What we are building

The Governance team at Domino is aimed at making Domino become the leading platform that enables enterprises to govern their AI projects effectively and efficiently. This is meant to  support innovation cycles for our customers, while allowing them to build models and outputs responsibly and safely.

The Data Product team at Domino is responsible for developing the integrations with services, systems and sources that data scientists rely on for their model development and analytics work. Our mission is to make access to data seamless and comprehensive such that data scientists can incorporate data into their workflows without worrying about how. We take satisfaction in making sure that data scientists can derive insights and produce predictions knowing that they have the full availability of data without worrying about engineering complexity.

The two teams have been working interchangeably and smoothly to fulfill their respective missions.

What your impact will be

In your first year, you will:

• Design and develop software solutions that improve Domino’s user experience with regards to the new, highly-sought Governance feature
• Extend and contribute enhancements to the open-source software powering Domino
• Create technical designs and clearly communicate them to cross-functional stakeholders
• Enable fellow engineers to achieve high quality through design and code reviews
• Work with product managers to ensure solutions are well planned and delivered on-time

What we look for in this role

• 5+ years of experience in backend development, especially in distributed computing with Hadoop, Spark, Kafka
• Experience in API design and development, e.g. RESTful APIs, GraphQL and similar, with a focus on integrating backend and front-end systems
• Strong proficiency in Java, Scala, Go, or Python, dedicated to building high-performance backend solutions
• Skilled in enhancing backend performance in cloud environments, well-versed in Docker, Kubernetes, and CI/CD practices.
• Experience building production grade software, preferably in Scala or Go.
• Comfortable in dynamic, fast-paced settings, possessing strong problem-solving skills and a comprehensive understanding of system design and architecture
• Strong familiarity with service-oriented architecture (SOA), adept at designing and developing modular, maintainable services for seamless system integration
• Ability to work independently and be a reliable teammate

What we value

• We value a growth mindset. High-performing creative individuals who dig into problems and see the opportunities for success
• We believe in individuals who seek truth and speak the truth and can be their whole selves at work
• We value all of you that believe improving is always possible At Domino Everything is a work in progress – we can do better at everything
• We emphasize an environment of teaching and learning to equip employees with the tools needed to be successful in their function and the company
• We strongly believe in the value of growing a diverse team and encourage people of all backgrounds, genders, ethnicities, abilities, and sexual orientations to apply

#LI-Remote

About the Role

This role requires high-level autonomy, where you will define your own direction within strategic goals, influence multiple teams, and navigate high complexity and ambiguity to deliver tangible business results. You will bridge the gap between technical innovation and real-world business challenges by applying advanced statistical and machine learning techniques to build predictive models and derive actionable insights.

What we are looking for

• Experience & Education: Bachelor's degree in Computer Science or Engineering with 8–12 years of relevant experience leading complex technical projects with multi-team impact.
• Advanced ML & Statistics: Expert capability in designing end-to-end ML systems for complex problems (including imbalanced data and concept drift), applying advanced statistical methods (mixed models, survival analysis, Bayesian approaches), and demonstrating a deep understanding of causal inference techniques, experimentation design (A/B testing, MABs), and deep learning architectures for unstructured data (e.g., NLP, Computer Vision).
• Core Technology Stack: Expertise in Python (including libraries like Pandas and NumPy) or R, proficiency with cloud platforms and their data/ML services (e.g., AWS Sagemaker, GCP Vertex AI, Azure ML Services), experience with big data technologies (e.g., Spark, Hadoop), and familiarity with MLOps tools (e.g., Kubeflow, MLflow, Docker).
• Business Immersion: Proven ability to immerse in operations to acquire deep domain expertise, thinking like an insider to translate seamlessly between business needs and engineering solutions.
• Full-Stack Agility: Ability to build complete applications rapidly across any technology stack, selecting the right tools to balance technical debt with delivery speed.
• Data Strategy & Integration: Experience architecting scalable data strategies across diverse teams and complex enterprise landscapes. This includes defining data governance policies, mastering data quality frameworks, building robust integration solutions for undocumented schemas and disparate systems, and driving the adoption of modern data warehousing and lakehouse architectures.
• AI Evaluation & Governance: Experience leading AI governance initiatives, creating evaluation standards, and training teams on rigorous AI verification and risk management.
• Rapid Prototyping: Proficiency in leading rapid delivery initiatives using a prototype-first approach to validate solutions quickly before scaling.
• Problem Discovery: A track record of seeking out undefined problems, embedding with users to discover latent needs, and turning ambiguity into clear problem statements.
  
  

Who you are

• Polymath Oriented: You bridge gaps between engineering, design, business, and science, rapidly immersing yourself in new domains to speak the language of the business.
• Curious Explorer: You seek out ambiguity rather than avoiding it, driving team curiosity through challenging questions and creating an environment that encourages experimentation.
• Outcome Owner: You drive an accountability culture focused on business impact rather than just deliverables, owning relationships and making trade-offs between custom solutions and generalizable work.
• Systems Thinker: You map complex system interactions across technical and business domains, anticipating cascading effects and understanding how technology changes impact operations.
• Precise Communicator: You translate seamlessly between technical and business language, ensuring requirements are clear enough to enable AI generation and facilitate productive discussions with stakeholders.
• Problem Discoverer: You embed with users to discover latent needs, turning ambiguity into clear problem statements rather than waiting for defined tasks.

What Appnovation Offers 

• Challenging and rewarding work with real impact
• Direct Access to Cutting-Edge AI Platforms
• Diverse and Inclusive Culture 
• Growth opportunities for personal and professional development
• A collaborative and innovative work environment where your ideas are valued
• Exposure to exciting projects and high-profile clients
• Supportive work environment with access to company leaders
• Hybrid working model

We're looking for a AI Engineer to join AI & Data Team. The Senior AI Engineer will lead the design, development, and deployment of cutting-edge AI solutions particularly those involving Large Language Models (LLMs), intelligent agents, and AI systems that integrate seamlessly into production-grade software. This person will work independently with minimal supervision while collaborating across teams to deliver intelligent, scalable, and reliable applications.

They will be responsible not only for crafting models and prototypes, but also for embedding these into robust applications, building APIs, writing clean code, and ensuring the operational success of AI-powered features in real-world environments.

The main objective of this role is to bridge AI innovation and software engineering excellence deploying LLMs, agents, and smart systems into full-stack products and production infrastructure.

What are the main responsibilities of this role? 

• Design and implement LLM-based solutions (e.g., RAG systems, multi-agent tools, copilots).
• Fine-tune, evaluate, and deploy open-source and proprietary foundation models.
• Build end-to-end AI-powered features in collaboration with product and engineering teams.
• Create scalable APIs and services to expose AI functionality to frontend/mobile clients.
• Own the lifecycle of AI features from experimentation to deployment and monitoring.
• Collaborate with DevOps to implement CI/CD pipelines and infrastructure-as-code for AI services.
• Implement observability, error tracking, and performance monitoring on deployed AI features.
• Evaluate and experiment with diverse AI frameworks and libraries (LangChain, Transformers, OpenAI SDKs, etc.).
• Lead technical discussions and mentor peers in AI best practices and architecture.
• Identify AI-driven product opportunities by working closely with clients or internal teams.

Which skills and experience do we envision for this role?

• +4 years of experience in AI/ML-focused roles with shipped software products.
• Hands-on experience with LLMs (OpenAI, Claude, Mistral, open-source models) and agent frameworks (LangChain, AutoGPT, etc.).
• Proficiency in prompt engineering, vector databases (Pinecone, Weaviate, etc.), and embedding strategies.
• Experience with testing AI systems using tools such as Promptfoo, Giskard, etc
• Solid Python skills, with experience building APIs using FastAPI, Django, or similar.
• Production experience with cloud platforms (AWS, GCP, or Azure) and tools like Docker, Terraform, Kubernetes.
• Good software development practices: testing, code reviews, version control (Git), and modular design.
• Experience deploying and monitoring AI systems in production.
• Familiarity with MLOps and model deployment workflows is a plus.

Join our team and enjoy:

• Access to Rootstrap University,Conferences/Certifications, and a Mentorship Program for your professional growth.
• Learning Bonus 
• Opportunities to organize cross-functional initiatives and receive 360 Feedback to improve your skills continuously.
• The flexibility to work remotely or from our offices in Montevideo, Buenos Aires, and Medellín, with a flexible time schedule and Workation program.
• Gym benefits, psychological counseling, and weekly lunch reimbursements with special foods in the offices.
• An Onboarding kit and access to cutting-edge technologies and tools to make your work easier.
• Referrals program bonuses, After offices, Prizes, and special occasions gifts to celebrate your achievements.
• People Care referent to support your well-being and personal development.

We value your well-being and happiness. We grow together!

How we work:

• We offer a flexible and diverse work environment that fosters multicultural talent
• We value autonomy and creativity, We want people to bring ideas to the table and to think as leaders.
• We put our focus on real human connections
• We have a commitment to excellence

At Rootstrap, we help companies launch, grow, and scale their digital products and services. With over 290 global team members, we’ve built solutions for industry leaders like MasterClass, Universal, and Google, as well as for innovative startups and inspired pioneers. Our team takes pride in making an impact that goes beyond the code, earning regular recognition as a Fastest Growing Company and Best B2B Service Provider.

We embrace an AI-first mindset, continuously exploring how artificial intelligence can enhance the way we build products, solve problems, and deliver value. Across all stages of the development process, we encourage our teams to actively leverage AI to boost productivity and drive innovation.

With offices in Uruguay, Argentina, and Colombia, we offer the flexibility to work remotely. Discover more about life at Rootstrap at https://www.rootstrap.com/careers.

All of our candidate searches are covered under Uruguayan Law 19691 (the purpose of this law is to promote the inclusion of people with disabilities).

As part of our Security and Compliance process, all seniority levels and roles must follow the company’s information security policies and secure development practices, and promptly report any suspected security incidents or vulnerabilities.

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