Junior Agentic Automation Engineer

We are looking for a Junior Agentic Automation Engineer to join our team and help build intelligent workflows powered by LLMs and agentic platforms. In this role, you will work alongside experienced engineers to implement, adapt, and monitor automation solutions that integrate AI agents with real-world data sources and tools. This is an excellent opportunity for someone curious about agentic AI who wants to grow their skills in a supportive environment.

Responsibilities

• Implement and adapt workflows using established patterns on platforms such as n8n, AgentKit, or UiPath Agentic Automation, working from existing designs and adjusting them to new use cases, with supervision for troubleshooting and bigger changes
• Work with prompts and model parameters, including basic prompt types (system and user) to meet the agent's logic requirements, and tune parameters such as temperature, top_p, and max_tokens
• Apply core AI concepts, including how LLM context windows work and how their size affects performance, how tokenization influences processing and cost, and how multi-modal models handle text and images
• Set up basic agents that integrate prompts, APIs, data sources, and agentic tools, with support from the team when needed
• Configure data grounding (RAG) by connecting agents to existing data sources such as Google Sheets, Microsoft SharePoint, and databases, so the AI can use your data
• Follow security basics by avoiding exposure of PII in prompts or logs and storing credentials in environment variables
• Use logging and monitoring tools by working with existing dashboards in the platform to track workflow runs, errors, and anomalies

Requirements

• At least 6 months of experience in a technical role, with willingness to learn and ask questions; no prior agentic or LLM experience required, but curiosity and basic technical literacy are essential
• Understanding of core agentic automation concepts and how to implement them in at least one platform such as n8n, AgentKit, or UiPath
• Knowledge of prompt engineering basics, including system vs. user prompts and simple prompt design for agent behaviour
• Familiarity with LLM fundamentals, including context windows, tokenization, and multi-modal inputs, as well as the effect of key parameters such as temperature and max_tokens
• Skills in basic RAG and data grounding, including connecting agents to spreadsheets, SharePoint, and databases
• Competency in security hygiene, including PII handling, credential management, and use of platform logging and monitoring
• Capability to follow established patterns and security practices, take direction from the team, and escalate when something is unclear or out of scope
• Demonstrated steady progress in understanding context windows, tokens, prompts, and how agents use tools and data
• Fluency in English, both written and spoken, at a minimum B2 level