Undergraduate AI Engineer, Internship Programme

Jobtailor; Greater London; On-site; GBP 18,000 - 28,000; Part time

Job summary

Jobtailor is seeking a motivated Master’s student to support AI/ML model development and LLM integrations in an enterprise environment. The role involves data collection, preprocessing and collaboration across engineering, product and data teams.

You will assist with model training and evaluation, participate in testing and debugging of AI-enabled solutions, and contribute to data pipelines, prompts and agent workflows. Excellent communication and learning agility are essential.

Qualifications

• Master’s student in Computer Science, Machine Learning, Data Science or related field.
• Strong Python fundamentals and data processing knowledge.
• Understanding of ML concepts: training, evaluation, feature engineering.
• Experience with AI systems such as LLM APIs, prompt-based interactions, or NLP.
• Familiarity with responsible AI, security, governance, and reliability considerations.
• Excellent communication and collaborative abilities.

Responsibilities

• Support development and integration of AI/ML models and LLMs into production-like systems.
• Assist with data collection, preprocessing, transformation, and management for model training and evaluation.
• Participate in testing, debugging, and improving AI-enabled solutions for performance and reliability.
• Contribute to training workflows, inference endpoints, data pipelines, and AI agents.
• Collaborate with engineering, product, data, risk, security, and business partners.
• Document model parameters, data pipelines and technical decisions.
• Engage in Agile practices and team ceremonies.
• Help ensure AI systems align with reliability, safety, governance and compliance.

Job description

• Support the development and integration of AI/ML models, LLM integrations, or intelligent services into controlled or production-like systems under guidance
• Assist with data collection, preprocessing, transformation, and management for model training, testing, validation, and evaluation
• Contribute to testing, debugging, and improving AI-enabled solutions for performance, reliability, explainability, and maintainability
• Support model training workflows, inference endpoints, prompt-based interactions, evaluation routines, data retrieval pipelines, AI agents, and agentic workflows
• Collaborate with engineering, product, data, risk, security, and business partners to implement AI-driven solutions
• Document model parameters, prompts, evaluation assumptions, data pipelines, system integrations, and technical decisions
• Participate in Agile practices including sprint planning, stand-ups, demos, retrospectives, code reviews, and team ceremonies
• Help ensure AI systems align with enterprise expectations for reliability, safety, governance, security, and compliance
• Develop foundational experience with APIs, cloud environments, data platforms, CI/CD, containers, deployment patterns, and monitoring
• Learn how AI-enabled software and machine learning solutions are designed, built, tested, and delivered in an enterprise environment

Requirements

• Currently enrolled in a Master’s degree program in Computer Science, Machine Learning, Data Science, Computer Engineering, or another technical field
• Knowledge of Python and foundational data processing technologies
• Foundational understanding of computer science concepts, including data structures, algorithms, debugging, testing, and problem solving
• Understanding of machine learning concepts such as model training, evaluation, feature engineering, and experimentation
• Experience using modern AI systems such as LLM APIs, prompt-based interactions, retrieval patterns, or generative AI applications
• Awareness of responsible AI, security, governance, compliance, and reliability considerations
• Strong communication, collaboration, documentation, and learning agility with the ability to work effectively in a team environment
• Preferred experience through academic coursework, research, projects, open-source contributions, internships, or extracurricular activities using Python, R, Java, JavaScript, or similar technologies
• Preferred familiarity with NLP techniques, embeddings, BERT, transformers, APIs, data pipelines, ETL, cloud environments, containerized development, CI/CD, version control, testing, and code reviews
• Preferred experience with AI-powered applications, LLM integrations, prompt engineering, retrieval-augmented generation, AI agents, agentic workflows, ML algorithms, and practical AI/ML problems
• Familiarity with cybersecurity and AI security concepts, including secure software development, application security, identity and access management, data protection, encryption, secure API design, threat modeling, prompt injection, data leakage, and model abuse

Core Competencies

Demonstrates foundational experience in AI/ML model development, data processing, and integration within enterprise environments. Proficient in Python and familiar with modern AI systems, cloud technologies, and Agile practices.

Highest-signal resume keywords

• Python Programming
• Machine Learning Concepts
• AI/ML Model Training
• Data Processing Technologies
• Agile Methodologies

ATS Optimization Keywords

Hard Skills

• Data Structures
• Algorithms
• Debugging
• Testing
• Feature Engineering
• NLP Techniques
• ETL
• CI/CD
• Version Control
• AI Algorithms

Soft Skills

• Communication
• Collaboration
• Documentation
• Learning Agility
• Teamwork

Industry Keywords

• Responsible AI
• Security
• Governance
• Compliance
• Reliability
• Cybersecurity
• Application Security
• Data Protection
• Encryption
• Threat Modeling

Tools & Technologies

• APIs
• Cloud Environments
• Data Platforms
• Containers
• Monitoring Tools
• LLM APIs
• Generative AI Applications
• Retrieval Patterns
• Prompt Engineering
• Agentic Workflows