Machine Learning Engineer

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Cartago, El Guarco, El Tejar, Sabana
In-Office
Fintech • Consulting
The Role

We're looking for a talented Machine Learning Engineer to join our team and build innovative AI solutions. You'll be responsible for developing and deploying sophisticated machine learning models, optimizing data pipelines, and contributing to cutting-edge projects. We're particularly interested in candidates with experience in designing, building and deploying machine learning systems, as this skill set will be a major advantage in pushing the boundaries of our AI capabilities.

What you’ll do 
  • Research innovative data solutions from unstructured sources  to solve real business problems.

  • Design, implement, and maintain scalable data pipelines for data ingestion, transformation, and delivery to ML models.

  • Develop and deploy advanced machine learning models using techniques like natural language processing, and computer vision.

  • Collaborate with a team of engineers, data scientists, and product managers to deliver impactful AI solutions.

  • Develop analytical approaches to meet business requirements; this involves translating requests into use cases, test cases, preparation of training data sets and iterative algorithm development 

  • Implement monitoring and logging solutions to track model performance and detect anomalies

  • Remain current on new developments in data analytics, Big Data, predictive analytics, and technology.

What experience you need  
  • Bachelor’s Degree in Statistics, Mathematics, Computer Science (preferably) or equivalent to experience

  • At least 4  years of experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark

  • At least 4 years of experience coding with several programming languages: Python, Java, SAS, R

  • At least 4 years of experience in developing and deploying machine learning algorithms in production, creating and using advanced Machine Learning algorithms: including supervised, unsupervised, and reinforcement learning techniques.

  • At least 4 years of experience in data science and statistics: understanding of data cleaning, preprocessing, feature engineering, and statistical analysis and using popular frameworks such as TensorFlow, PyTorch or scikit-learn

  • 2+ years of experience working with cloud platforms (e.g. GCP, AWS, Azure) for deploying and managing ML systems.

What could set you apart
  • GCP, AWS or Azure cloud certifications

  • Experience with large language models (LLMs) and their applications in various domains.

  • Knowledge of prompt engineering, fine-tuning, and evaluation techniques for LLMs.

  • Familiarity with NLP tasks such as text classification, sentiment analysis, or data extraction.

  • Experience with Agile development methodologies (SCRUM, XP, etc)

  • Strong communication and collaboration skills: Ability to work effectively in a team environment and communicate technical concepts to both technical and non-technical stakeholders.

  • Strong analytical, problem solving and diagnostic skills

  • Ability to translate technical requirements into automation solutions (machine learning pipelines)

  • Strong communication and collaboration skills for working effectively in a team environment.

  • Self-driven and self managed according to changing priorities

#LI-DU1
#LI-Hybrid

Primary Location:

CRI-Sabana

Function:

Function - Tech Dev and Client Services

Schedule:

Full time

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The Company
HQ: Atlanta, GA
16,742 Employees

What We Do

At Equifax (NYSE: EFX), we believe knowledge drives progress. As a global data, analytics, and technology company, we play an essential role in the global economy by helping financial institutions, companies, employers, and government agencies make critical decisions with greater confidence. Our unique blend of differentiated data, analytics, and cloud technology drives insights to power decisions to move people forward.

Headquartered in Atlanta and supported by nearly 15,000 employees worldwide, Equifax operates or has investments in 24 countries in North America, Central and South America, Europe, and the Asia Pacific region.

For more information, visit Equifax.com.

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