Junior AI Engineer

About the Job you are considering:

We are looking for a Junior AI Engineer to design, build, and operationalize AI and Agentic AI solutions that transform end-to-end enterprise processes. You will develop scalable AI-powered applications, intelligent agents, and RAG-based solutions using LLMs and modern AI engineering practices. Working closely with Solution Architects, Forward Deployed Engineers, and business teams, you will integrate AI solutions into enterprise platforms and workflows.

The role requires a strong foundation in software engineering, AI/ML deployment, MLOps, and Responsible AI to deliver secure, reliable, and production-ready solutions.

Hybrid working:

The places that you work from day to day will vary according to your role, your needs, and those of the business; it will be a blend of Company offices, client sites, and your home; noting that you will be unable to work at home 100% of the time.

Your Role:

• Design, develop, and deploy AI-powered applications leveraging LLMs, Generative AI, and Agentic AI technologies to transform enterprise processes.
• Build and orchestrate intelligent agent workflows capable of reasoning, planning, and executing actions across enterprise platforms and business systems.
• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, prompt engineering strategies, and model performance for scalability, reliability, and cost efficiency.
• Integrate AI solutions with APIs, data platforms, workflow engines, and enterprise applications to enable seamless business operations.
• Operationalize AI systems through MLOps practices, including deployment, monitoring, evaluation, observability, and lifecycle management.
• Collaborate with architects, engineers, and business stakeholders to ensure secure, governed, and production-ready AI solutions aligned with enterprise standards and Responsible AI principles.

Your Skills:

• Experience with Agentic AI frameworks, orchestration platforms, and multi-agent system development.
• Familiarity with Large Language Models (LLMs), vector databases, RAG architectures, and prompt engineering techniques.
• Hands-on experience with MLOps practices, including model deployment, monitoring, observability, and lifecycle management.
• Knowledge of cloud-based AI services and platforms such as Microsoft Azure AI, AWS, or Google Cloud AI.
• Experience in process automation, intelligent workflows, decision-support systems, and enterprise application integration.
• Understanding of Responsible AI principles, including explainability, governance, security, privacy, and compliance requirements.

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