AI Engineer

Job Description

We’re looking for an enthusiastic AI Engineer to join a forward-thinking global technology environment where you’ll work on real-world AI solutions, experiment with emerging technologies and help turn exciting AI concepts into practical business applications.

This is a fantastic opportunity for someone early in their AI career who wants exposure to Large Language Models, Generative AI, AI Agents and Retrieval-Augmented Generation (RAG) while working alongside experienced AI engineers, architects and business teams.

Requirements

ESSENTIAL SKILLS:

• Foundational knowledge of Large Language Models (LLMs) and how they are used in production.
• Experience with prompt engineering and designing prompt workflows and guardrails.
• Familiarity with Retrieval-Augmented Generation (RAG) concepts and connecting LLMs to documents/databases.
• Basic software development skills (Python preferred) and version control (Git).
• Ability to design and execute test plans for AI systems, including quality and latency measurements.
• Strong analytical and problem-solving skills with attention to detail.
• Clear written and verbal communication skills for working with technical and non-technical stakeholders.
• Knowledge of data handling best practices, security awareness, and privacy considerations.

ADVANTAGEOUS

SKILLS:

• Hands-on experience with agent frameworks such as Copilot, Lang Graph, or Semantic Kernel.
• Experience implementing RAG pipelines and vector search (e.g., FAISS, Pinecone, Milvus).
• Familiarity with evaluation metrics for LLMs (hallucination measurement, relevance, answer quality).
• Exposure to cloud platforms and deployment tooling (Azure, AWS, or GCP).
• Understanding of MLOps/MLOps-lite practices for model deployment and monitoring.
• Experience integrating AI with enterprise applications (SAP, Service Now, SharePoint, Teams).
• Experience with data engineering basics: ETL, data preprocessing and feature extraction.
• Familiarity with automated testing frameworks and CI/CD for AI components.

QUALIFICATIONS/

EXPERIENCE:

• Postgraduate degree (Master’s or higher) in Computer Science, Engineering, Statistics or a closely related field.
• Demonstrable coursework, projects, or internships involving LLMs, NLP or applied ML.
• Strong foundational programming skills (Python) and familiarity with AI/ML tool chains and SDKs.

Benefits

• Cutting edge global IT system landscape and processes.
• Flexible working of 1960 hours in a 12-month period.
• High Work-Life balance.
• Remote / On-site work location flexibility.
• Highly motivating, energetic, and fast-paced working environment.
• Modern, state-of-the-art offices.
• Dynamic Global Team collaboration.
• Application of the Agile Working Model Methodology.

Requirements
ESSENTIAL

SKILLS:

Foundational knowledge of Large Language Models (LLMs) and how they are used in production.

Experience with prompt engineering and designing prompt workflows and guardrails. Familiarity with Retrieval-Augmented Generation (RAG) concepts and connecting LLMs to documents/databases. Basic software development skills (Python preferred) and version control (Git). Ability to design and execute test plans for AI systems, including quality and latency measurements. Strong analytical and problem-solving skills with attention to detail. Clear written and verbal communication skills for working with technical and non-technical stakeholders.

Knowledge of data handling best practices, security awareness, and privacy considerations. ADVANTAGEOUS

SKILLS:

Hands-on experience with agent frameworks such as Copilot, Lang Graph, or Semantic Kernel. Experience implementing RAG pipelines and vector search (e.g., FAISS, Pinecone, Milvus). Familiarity with evaluation metrics for LLMs (hallucination measurement, relevance, answer quality). Exposure to cloud platforms and deployment tooling (Azure, AWS, or GCP). Understanding of MLOps/MLOps-lite practices for model deployment and monitoring. Experience integrating AI with enterprise applications (SAP, Service Now, SharePoint, Teams).

Experience with data engineering basics: ETL, data preprocessing and feature extraction. Familiarity with automated testing frameworks and CI/CD for AI components. QUALIFICATIONS/

EXPERIENCE:

Postgraduate degree (Master’s or higher) in Computer Science, Engineering, Statistics or a closely related field. Demonstrable coursework, projects, or internships involving LLMs, NLP or applied ML. Strong foundational programming skills (Python) and familiarity with AI/ML tool chains and SDKs.