Junior AI Engineer

Junior AI Engineer

We are looking for a

Junior AI Engineer to join our AI Practice.

In this role, you will help design, configure, test, and improve AI agents. You will investigate how agents behave, identify where they succeed or fail, and use experiments and data analysis to make their results more reliable and useful.

Junior AI Engineer

We are looking for a Junior AI Engineer to join our AI Practice. In this role, you will help design, configure, test, and improve AI agents. You will investigate how agents behave, identify where they succeed or fail, and use experiments and data analysis to make their results more reliable and useful. You will also contribute to feasibility studies for new AI initiatives. This means understanding the business problem, exploring available data, testing possible solution approaches, and documenting what you learn. When an experiment proves valuable, you will work with more experienced team members to turn it into a maintainable solution that can be used by customers

What you will do...

Build and configure AI agents

• Set up AI agents using code-based tools
• Configure instructions, prompts, knowledge sources, tools, and orchestration flows
• Connect agents to approved business data and services
• Test how agents behave across expected, unexpected, and incomplete user requests
• Document agent configurations so experiments can be understood and repeated

Analyse and improve agent behaviour

• Review agent responses, execution traces, tool calls, and logs
• Identify recurring failure patterns such as incorrect answers, weak reasoning, poor source use, or unsuccessful tool selection
• Create representative test questions and evaluation datasets
• Define practical criteria for assessing answer quality, reliability, and usefulness
• Compare alternative prompts, models, tools, retrieval approaches, or orchestration patterns
• Turn findings into clear recommendations and improvement actions

Run feasibility studies

• Work with use-case leads, engineers, customers, and subject matter experts to understand a business problem
• Identify the questions that a feasibility study needs to answer
• Explore available data using Python, SQL, or visualization tools
• Assess data quality, completeness, relevance, and limitations
• Build small prototypes to test whether an idea is technically and practically viable
• Compare possible solution approaches and describe their trade-offs
• Present findings, risks, assumptions, and recommended next steps

Productize successful experiments

• Help move promising prototypes from notebooks or isolated experiments into reusable solution components
• Write clear, maintainable Python code
• Add tests, logging, configuration, and documentation
• Contribute to evaluation and regression pipelines
• Use version control and follow the team’s engineering practices
• Support deployment, monitoring, and troubleshooting
• Improve solutions based on evaluation results and feedback from real users
• Share learnings so that successful approaches can be reused across other use cases

Collaborate and learn

• Work closely with AI engineers, platform engineers, product owners, delivery leads, and domain experts.
• Ask questions early when requirements, data, or expected outcomes are unclear
• Communicate findings in plain language to technical and non-technical colleagues
• Participate in code reviews, design discussions, demonstrations, and retrospectives
• Keep up with relevant developments in AI agents, language models, evaluation, and supporting tools
• Gradually take ownership of small use cases or defined parts of a larger solution

What we are looking for...

Essential skills

• A bachelor’s or master’s degree in computer science, artificial intelligence, data science, software engineering, or a related discipline, or equivalent practical experience
• Hands-on experience with Python through employment,internships, academic projects, or personal projects
• Knowledge of data analysis using tools such as Python and SQL
• Ability to approach an unclear problem through questions, hypotheses, experiments, and evidence.
• Willingness to write clean code, tests, and technical documentation
• Experience with Git and automated development pipelines
• Clear communication skills and the ability to explain what you tried, what happened, and what you recommend
• Curiosity, attention to detail, and a willingness to learn from unsuccessful experiments
• Ability to work collaboratively and accept guidance and feedback

Use