Senior Machine Learning Engineer

Tiger Analytics; Atlanta (GA); Remote; USD 100,000 - 150,000; Full time

Job summary

A leading analytics consulting firm seeks experienced Machine Learning Engineers to enhance their global team. In this role, you will deploy scalable ML solutions, manage data pipelines, and collaborate across teams to deliver business value. Ideal candidates will possess strong technical skills, extensive experience, and a commitment to excellence in a rapidly evolving environment. This role offers opportunities for significant career development in a dynamic and entrepreneurial setting.

Qualifications

• 7+ years of experience in Machine Learning or Data Science.
• Ability to maintain scalable ML solutions in production.
• Excellent communication and teamwork skills.

Responsibilities

• Provide solutions for deployment and improvement of data science solutions.
• Create scalable Machine Learning systems and build reusable data pipelines.
• Write production-quality code and troubleshoot ML model issues.

Skills

Python

Spark

Hadoop

Docker

Machine Learning frameworks

Big Data

Cloud environments

Education

Bachelor's degree in Computer Science or related field

Tools

MLflow

Airflow

Kubernetes

AWS SageMaker

Job description

Tiger Analytics is looking for experienced Machine Learning Engineers to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.

We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world. You will be responsible for:

• Providing solutions for the deployment, execution, validation, monitoring, and improvement of data science solutions
• Creating Scalable Machine Learning systems that are highly performant
• Building reusable production data pipelines for implemented machine learning models
• Writing production-quality code and libraries that can be packaged as containers, installed and deployed

You will collaborate with cross-functional teams and business partners and will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.

• Bachelor's degree or higher in computer science or related, with 7+ years of work experience
• Ability to collaborate with Data Engineers and Data Scientists to build data and model pipelines and help run machine learning tests and experiments
• Ability to manage the infrastructure and data pipelines needed to bring ML solutions to production
• End-to-end understanding of applications being created
• Ability to maintain scalable machine learning solutions in production
• Ability to abstract the complexity of production for machine learning using containers
• Ability to troubleshoot production machine learning model issues, including recommendations for to retrain and revalidate
• Experience with Big Data Projects using multiple types of structured and unstructured data
• Ability to work with a global team, playing a key role in communicating problem context to the remote teams
• Excellent communication and teamwork skills

Additional Skills Required

• Python, Spark, Hadoop, and Docker with an emphasis on good coding practices in a continuous integration context, model evaluation, and experimental design
• Knowledge of ML frameworks like Scikitlearn, Tensorflow, and Keras
• Knowledge of MLflow, Airflow, and Kubernetes
• Experience with Cloud environments and knowledge of offerings such as AWS SageMaker
• Proficiency in statistical tools, relational databases, and expertise in programming languages (Python/SQL)

This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.

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