Software Engineer AI/ML

Roles and Responsibilities

• Design, build, deliver, and maintain AI/ML products including LLM-powered applications, forecasting models, anomaly detection systems, and intelligent agents.
• Own the full AI/ML lifecycle: requirements analysis, model design, training, evaluation, API development, deployment, and operational support.
• Convert complex operational datasets into scalable AI capabilities that enable real-time decision support.

Required Qualifications

• Bachelor's Degree in Computer Science, Data Science, Statistics, Engineering, or related field from an accredited college or university.
• Minimum of 3 years of hands-on AI/ML engineering experience building and deploying machine learning models and/or AI-powered applications to production.

Desired Characteristics

• Technical Expertise: Write production-quality code that meets standards and delivers intended functionality using the most appropriate technologies for the project (e.g., Python, Java, C#, TypeScript—based on system needs).
• Domain & Business Acumen: Experience building AI/ML solutions for supply chain, manufacturing, maintenance, or operations analytics is a strong plus. Understands business metrics and can translate AI/ML capabilities into quantifiable business outcomes (cost savings, time reduction, forecast accuracy improvement).
• Leadership & Collaboration: Leads by example through delivering AI/ML products while mentoring team on AI integration, prompt engineering, and model usage. Works seamlessly with BI developers, platform engineers, and business stakeholders.

Personal Attributes

• Strong written and verbal communication skills with the ability to explain complex AI/ML concepts simply and translate effectively between data scientists, software engineers, and business stakeholders.
• Business-minded approach that focuses on operational metrics, user needs, and business impact while designing AI solutions that solve real problems rather than technical exercises.
• Persists to completion by driving AI/ML products through deployment, monitoring, and iteration while taking ownership of model performance and continuously improving accuracy.