AI Engineer  

Key Responsibilities

• Understand the business problem, POC objectives, and evaluation metrics
• Profile and clean sample datasets for experimentation (lightweight data prep)
• Build and test simple pipelines for data ingestion, prompt construction, and output evaluation.
• Design experiments to test different model configurations, prompts, or retrieval strategies.
• Analyse Gen AI outputs for quality, accuracy, and alignment with requirements.
• Identify common failure modes (hallucination, bias, irrelevant answers, factual errors).
• Provide insights and recommendations to improve model performance in quick iterations.
• Support SMEs in defining ground truth benchmarks for evaluation.
• Collaborate with developers on integrating models into the POC workflow.

Skills / Competencies

• Understanding of Gen AI concepts (tokenization, embeddings, RAG, prompting, evaluation).
• Familiar with AWS Bedrock services
• Ability to do rapid experimentation rather than perfect models.
• Basic proficiency in Python and Gen AI tools (e.g., model SDKs, vector DBs).
• Analytical mindset: can quantify subjective output (accuracy, relevance, readability).
• Good data wrangling skills to prepare small datasets quickly.

• Strong analytical and planning skills;
• Good communication and presentation skills;
• Excellent problem-solving skills;
• Understanding of Gen AI concepts (tokenization, embeddings, RAG, prompting, evaluation).
• Familiar with AWS Bedrock services
• Ability to do rapid experimentation rather than perfect models.
• Basic proficiency in Python and Gen AI tools (e.g., model SDKs, vector DBs).