Machine Learning Engineer (for LATAM candidates)

Senior Machine Learning Engineer
Employer: Kanda Software (www.kandasoft.com)
Full-time, long term, remote job
Start: ASAP
About the Role
We are looking for a Senior Machine Learning Engineer to join our Data Science and Engineering team and help design, build, and scale machine learning solutions used in production.
This role is ideal for an experienced engineer with a strong foundation in machine learning, deep learning, software engineering, and large-scale data systems. You will work closely with data scientists, ML engineers, and software engineers to take ML solutions from experimentation through production, building the pipelines, services, and infrastructure required to operate them reliably at scale.
The team works across a broad range of AI/ML problems, which may include predictive modeling, recommendation systems, computer vision, NLP, information retrieval, and emerging Generative AI applications.
Experience with LLMs, RAG, or AI agents is a plus, but is not a core requirement for this position.
What You'll Do
• Design, develop, and productionize machine learning and deep learning models for real-world applications.
• Build and optimize ML solutions using frameworks such as PyTorch and/or TensorFlow.
• Develop end-to-end batch and real-time data pipelines for training, inference, and large-scale data processing.
• Work with large structured and unstructured datasets, including feature engineering, data preparation, and model evaluation.
• Design scalable ML architectures and services capable of processing and serving large volumes of data.
• Build reliable, maintainable, and efficient production software primarily using Python and SQL.
• Deploy and operate ML models and services in distributed cloud environments such as AWS, GCP, or Azure.
• Collaborate with Data Scientists and Engineers to translate prototypes and research ideas into scalable production systems.
• Contribute to system architecture, technical design, project planning, and engineering decisions.
• Monitor and improve model and system performance, scalability, reliability, and maintainability.
• Evaluate and adopt new ML technologies where they provide practical value to the business.
• Mentor other engineers and contribute to engineering and ML development best practices.
Required Qualifications
• 5+ years of professional experience in Machine Learning Engineering, Data Science Engineering, or a closely related role.
• Bachelor's or advanced degree in Computer Science, Machine Learning, Engineering, Mathematics, or a related technical field, or equivalent practical experience.
• Strong hands-on expertise in machine learning and deep learning.
• Production experience with PyTorch and/or TensorFlow.
• Strong programming skills in Python and experience writing clean, maintainable, production-quality code.
• Experience developing and deploying ML models into production environments.
• Experience working with large datasets and building scalable data-processing or ML pipelines.
• Strong understanding of data structures, algorithms, system design, and software engineering principles.
• Experience designing or working with distributed systems and cloud-based architectures.
• Experience with at least one major cloud platform such as AWS, GCP, or Azure.
• Experience working with structured and unstructured data and designing appropriate data models and processing strategies.
• Strong analytical and problem-solving skills.
• Ability to communicate effectively and collaborate with Data Scientists, Engineers, Product teams, and other technical stakeholders.
Preferred Qualifications
Experience in several of the following areas would be valuable, but candidates are not expected to have all of them:
• Large-scale data processing technologies such as Apache Spark or Databricks.
• Containerization and orchestration technologies such as Docker and Kubernetes.
• MLOps practices, including model deployment, serving, monitoring, and lifecycle management.
• Real-time or streaming technologies such as Kafka, Kinesis, Spark Streaming, or Flink.
• Building scalable APIs, microservices, or model-serving infrastructure.
• Information retrieval and search technologies such as Elasticsearch, Lucene, or Solr.
• Recommendation systems, computer vision, NLP, forecasting, fraud detection, or other applied ML domains.
• Infrastructure as Code using technologies such as Terraform or CloudFormation.
• Experience with LLMs, Retrieval-Augmented Generation (RAG), Generative AI, or AI agents.
• Experience optimizing ML workloads for performance, latency, scalability, or cost.
What We're Looking For
Beyond specific technologies, we're looking for someone who combines strong ML fundamentals with strong engineering judgment.
The ideal candidate has experience taking machine learning beyond notebooks and prototypes: designing the surrounding data pipelines, services, deployment infrastructure, and production systems necessary to make ML solutions reliable and scalable.
You should be comfortable working in an iterative environment, collaborating across disciplines, making pragmatic technical decisions, and taking ownership of complex ML engineering problems from design through production.
Minimum English Level: C1 Advanced

Work arrangement
Yes