Golang Job: AI-ML Engineer

Position: AI/ML Engineer

Duration: 12 months

Location: Chicago, IL

Job Description

An AI/ML Engineer designs, develops, and deploys production-ready artificial intelligence and machine learning models and systems, managing data pipelines, running experiments, and training models using programming languages like Python and frameworks such as TensorFlow and PyTorch. This role requires strong technical skills in machine learning, programming, and algorithms, as well as effective collaboration with data scientists, software developers, and business stakeholders to align AI solutions with organizational goals.

Key Responsibilities

• Model Development: Research, design, and implement machine learning algorithms and applications to solve business problems.
• Data Pipeline Management: Build and manage scalable data pipelines for preprocessing, feature engineering, and data flow to train and evaluate ML models.
• Model Training and Deployment: Train, test, and tune machine learning models, ensuring they meet quality standards and deploying them into production environments.
• Performance Monitoring and Optimization: Monitor the performance of trained models, retrain them as needed, and optimize them for better performance.
• Collaboration: Work with cross-functional teams, including data scientists, software engineers, and product managers, to integrate AI into applications and achieve project goals.
• Stay Updated: Keep abreast of emerging AI and machine learning technologies and best practices to enhance institutional capabilities.

Required Skills & Qualifications

• Programming Languages: Proficiency in Python, Java, or R, Golang
• Machine Learning Frameworks: Expertise in tools and libraries like TensorFlow, PyTorch, and scikit-learn.
• Concepts: Deep understanding of machine learning, deep learning, data mining, algorithms, and computer vision.
• Tools: Familiarity with Git for version control and cloud platforms for scalable solutions.
• Soft Skills: 
• Collaboration: Ability to work effectively in multidisciplinary teams.
• Communication: Excellent communication skills to explain complex technical concepts to both technical and non-technical stakeholders. 
• Problem-Solving: Strong analytical and problem-solving skills to design and implement effective AI solutions.