AI/ML Engineer

Job Summary

The AI/ML Engineer develops and implements AI/ML models and solutions to drive business innovation and efficiency. This role involves developing and validating machine learning models, deep learning algorithms, and statistical analyses, collaborating with cross-functional teams, and ensuring the quality and impact of AI/ML solutions.

Responsibilities and Duties

• Develop and implement AI/ML projects,

including the design and development of models and algorithms.

• Collaborate with stakeholders to understand business requirements and translate them into AI/ML solutions.
• Develop and validate machine learning models, deep learning algorithms, and statistical analyses.
• Ensure the accuracy, quality, and relevance of AI/ML outputs.
• Stay updated with the latest advancements in AI/ML technologies and best practices, applying them to enhance solutions.
• Provide support and guidance to other team members as needed.
• Ensure compliance with data governance,

security, and regulatory standards in all AI/ML activities.

• Prepare and present AI/ML reports and documentation to senior management and stakeholders.
• Participate in project planning and contribute to the development of project timelines and deliverables.
• Perform other duties relevant to the job as assigned by the Sr. AI/ML Engineer or senior management.

Requirements

• Bachelor’s degree in AI/ML Engineering,

Computer Science, or a related field

• Relevant certifications (e.g., Google Cloud

Professional Machine Learning Engineer, AWS Certified Machine Learning –

Specialty) are preferred

• Preferred 1 year of experience in AI/ML engineering or related fields.
• Strong programming skills in languages such as Python, R, or Java
• Proficiency in AI/ML tools and frameworks

(e.g., TensorFlow, PyTorch)

• Excellent problem-solving and analytical skills
• Strong communication and interpersonal skills
• Attention to detail and commitment to quality
• In-depth understanding of AI/ML principles,

Machine learning algorithms, and statistical analysis

• Familiarity with AI/ML model deployment and monitoring
• Knowledge of data governance, security, and regulatory standards
• Ability to manage multiple tasks and prioritize effectively
• Strong attention to detail and commitment to delivering high-quality work
• Ability to work independently and as part of a team
• Programming languages (e.g., Python, R,

Java)

• AI/ML tools and frameworks (e.g.,

TensorFlow, PyTorch)

• Data visualization tools (e.g., Tableau,

Power BI)

• Collaboration and communication tools

(e.g., Slack, Microsoft Teams)

• Data management systems (e.g., SQL, NoSQL databases)