Junior AI/ML Engineer

Role Summary:

The Junior AI/ML Engineer contributes to the design, build, and delivery of end-to-end AI/ML solutions under the guidance of senior engineers. This role is engineering-first, applying data science and machine learning as tools within well-engineered software systems.

Engineers at this level focus on well-defined implementation tasks within a larger solution, learning the full lifecycle - design, development, validation, and production handoff - through pairing, code review, and structured mentoring.

AI & ML Development:

• Implement ML models and components against established designs, using structured, time-series, and unstructured data

• Run and document model validation, evaluation, and error analysis under senior guidance

• Build familiarity with the team's AI/ML techniques and how they are applied to engineering, quality, and product use cases

Software & Systems Engineering:

• Contribute production-quality code to AI systems, including:

• Data pipelines and feature engineering

• Model training and inference services

• Components of agentic solutions combining LLM and other systems

• Write clean, maintainable, and testable code (primarily Python), responding constructively to code review

• Use the team's shared AI/ML components and engineering frameworks

Delivery & Execution:

• Deliver well-scoped implementation tasks reliably, escalating blockers early

• Participate in requirement clarification and solution iteration with the team

• Support preparation of solutions for operationalization in partnership with MLOps teams

Growth Expectations:

• Progress toward independent ownership of implementation tasks end-to-end

• Develop breadth across data, modeling, and software concerns

• Actively seek and apply feedback from senior engineers

Basic Qualifications:

• Bachelor's degree in engineering, computer science, applied mathematics, or a related field

• A minimum of 1 year of experience

• Solid software engineering fundamentals

• Exposure to machine learning through coursework, internships, or projects

• Proficiency in Python; familiarity with common ML libraries

• Willingness to work across data, modeling, and software concerns

Preferred Qualifications:

• Internship or project experience deploying ML in real systems

• Exposure to cloud-based data or ML platforms

• Interest in LLM-based and agentic solutions

• Familiarity with software delivery practices (version control, CI, testing)