We are the financial technology company in Mexico that has helped more than 70k customers to make their plans come true.
Our purpose is to support small and medium enterprises in the country to fulfill their dreams, through our solutions (financing, credit card and payments) to help solve their main problems, seeking to be the best ally of entrepreneurs, contributing to the community, the country and the world
About the Role
We are looking for a detail-oriented Machine Learning Engineer, your primary responsibility will be to bridge the gap between the Data Science team and the production environment. You will work closely with Data Scientists, Data Engineers, and the Technology team to enable the seamless deployment of machine learning models and data products. Your expertise in programming, data engineering, and machine learning operations will empower Data Scientists to independently deliver their models to production. You will play a key role in enhancing the overall efficiency and scalability of our data science operations.
Collaborate with Data Scientists to understand their model development requirements and assist in translating these requirements into production-ready solutions.
Architect, build, test, deploy distributed, scalable, and resilient Spark/Scala/Kafka Data processing, and Machine Learning model pipelines for batch, micro-batch, and streaming workloads.
Develop and deploy robust, scalable, and maintainable machine learning services, APIs, and frameworks that enable Data Scientists to deploy their models to production efficiently.
Collaborate with Data Engineers to design and optimize data pipelines that feed the models in production, ensuring the availability and quality of data inputs.
Continuously explore and implement new tools, technologies, and frameworks that enhance the data science workflow and align with the needs of the team.
Champion and implement best practices for ML Ops, including model monitoring, model versioning, and performance optimization.
Collaborate with the Technology team to align the data science tech stack with the organization's overall technology stack and ensure integration with existing systems.
Stay up to date with the latest advancements in machine learning, data engineering, and related technologies, and apply them to improve the overall efficiency and effectiveness of the Data Science team.
Act as a mentor and provide technical guidance to Data Scientists, helping them improve their programming skills and adopt efficient development practices.
Collaborate with cross-functional teams to understand business requirements and translate them into actionable technical solutions.
Collaborate with data engineers to develop automated orchestration of data pipelines in order to provide the proper data to the data scientists in the respective stage (training and production)
Collaborate with data scientists to develop automated orchestration of model pipelines to solve Konfío business use case objectives
Problem Solver – You are proficient at using a combination of intuition and logic to
come up with solutions
Detail Oriented – You are an expert at identifying nuances and aligning small details
with larger objectives
Independent Worker – Your experience and capabilities allow you to require little instruction and guidance
Excellent team player – You are humble, ambitious and driven to make a difference
Excellent communication skills with the ability to collaborate effectively with cross-functional teams and explain complex technical concepts to non-technical stakeholders.
Bachelor's degree in Computer Science, Engineering, or a related field. A Master's or Ph.D. in a relevant field is a plus.
Job Title: Machine Learning Engineer 2
Strong programming skills in languages such as Python, Spark, or Scala, with experience in building scalable and production-ready software solutions.
Solid understanding of machine learning concepts, algorithms, and frameworks, with hands-on experience in developing and deploying machine learning models.
Proficiency in data engineering techniques, including data extraction, transformation, and loading (ETL), data validation, and database management.
Experience with big data technologies such as Apache Spark, Hadoop, or distributed computing frameworks.
Familiarity with cloud platforms such as AWS, Azure, or GCP, and experience deploying machine learning models in a cloud environment.
Experience with containerization technologies like Docker and orchestration frameworks like Kubernetes.
Knowledge of DevOps principles and experience with CI/CD pipelines for machine learning models.
Nice To Have
Experience with AWS services, particularly Amazon SageMaker, for developing, training, and deploying machine learning models in a cloud environment.
Knowledge of streaming data processing frameworks like Apache Kafka or Apache Flink.
Familiarity with data governance and security practices in a machine learning context.
Understanding of software engineering best practices, including code versioning, code review, and testing methodologies.