Equifax, Inc.
Machine Learning Engineer
Equifax, Inc., San Jose, California, United States, 95199
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 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).
Self-driven and self-managed according to changing priorities.
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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 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).
Self-driven and self-managed according to changing priorities.
#J-18808-Ljbffr