Machine Learning Engineer

Veriipro; Schaumburg (IL); On-site; USD 90,000 - 120,000; Full time

Job summary

Veriipro in Schaumburg, Illinois, is seeking a Machine Learning Developer to design and implement advanced machine learning models for data analysis and AI solutions. Candidates should have proficiency in Python and experience with leading ML libraries and techniques, including Generative AI and Large Language Models.

The role involves collaborating cross-functionally, integrating solutions with existing systems, and continuously improving model performance. Familiarity with cloud platforms and MLOps is a plus.

Qualifications

• Proficiency in Python programming, with strong knowledge of relevant libraries.
• Strong understanding of Linux operating systems and tools.
• Extensive experience with supervised and unsupervised learning methods.

Responsibilities

• Design and implement machine learning models for data analysis.
• Collaborate with teams to integrate ML solutions with existing systems.
• Continuously improve models through testing and optimization.

Skills

Proficiency in Python programming

Strong understanding of Linux

Experience in data analysis

Expertise in integrating ML solutions

Familiarity with version control systems

Tools

NumPy

Pandas

Scikit-learn

TensorFlow

PyTorch

Git

Jira

Confluence

Job description

Roles and Responsibilities

• Design, develop, and implement machine learning models for data analysis, predictive analytics, and AI-driven solutions.
• Utilize supervised and unsupervised learning techniques, including Generative AI (GenAI) and Large Language Models (LLMs), to build and optimize advanced ML systems.
• Perform data analysis, preprocessing, and feature engineering to support model training and integration.
• Collaborate with cross-functional teams to integrate ML solutions with source systems and ensure smooth data flow.
• Continuously improve models through iterative testing, tuning, and performance optimization.
• Contribute to the development of ML workflows, pipelines, and model deployment strategies.
• Stay updated on industry trends and emerging technologies in machine learning and AI.

Must-Haves

• Proficiency in Python programming, with strong knowledge of relevant libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, etc.
• Strong understanding of Linux operating systems and related tools for efficient development and deployment.
• Extensive knowledge and hands-on experience with supervised and unsupervised learning, as well as Generative AI (GenAI) and Large Language Models (LLMs).
• Experience in data analysis and building advanced machine learning models.
• Expertise in integrating ML solutions with source systems to ensure seamless data handling.
• Familiarity with version control systems (e.g., Git) and collaboration tools (e.g., Jira, Confluence).

Nice to Have

• Experience with cloud-based ML platforms (AWS, GCP, Azure).
• Knowledge of MLOps principles for model deployment and lifecycle management.