An AI Apprentice job description typically targets entry-level candidates, fresh graduates, or career switchers to support artificial intelligence, machine learning, and automation initiatives under the guidance of senior engineers.
- AI & Automation Support: Assist in building, testing, and optimizing AI, machine learning, and automation workflows (such as reporting automation, agentic pilots, and chatbots).
- Generative AI & LLMs: Support the integration of Large Language Models (LLMs), prompt engineering, retrieval-augmented generation (RAG), and vector databases.
- Data Preparation & Analysis: Collect, clean, preprocess, and analyze structured and unstructured datasets to feed into machine learning models
- Software Engineering: Write clean, maintainable code (primarily in Python or Java), develop REST APIs, and use version control tools like Git.
- Documentation & Quality Assurance: Document technical solutions, maintain query logs, and review AI-generated outputs for quality and accuracy.
Who can apply?
• Proficiency in Python or Java, alongside Object-Oriented Programming (OOP).
• Core Fundamentals: Strong grasp of Data Structures and Algorithms (DSA), SQL/databases, and basic statistics.
• AI/ML Knowledge: Familiarity with machine learning concepts, neural networks, and deep learning frameworks (such as PyTorch or TensorFlow).
• Tools & Tech: Experience or exposure to REST APIs, Git/GitHub, cloud platforms, or vector databases.
• Soft Skills: Eagerness to learn, strong problem-solving and analytical thinking, and effective team communication.
Skills you’ll bring
Experience, academic exposure, or strong interest in the following:
AI & LLM Technologies: Generative AI, AI Agents, OpenAI APIs, Amazon Bedrock, Prompt Engineering.
Software Development: Python, JavaScript, TypeScript, Java, Node.js, REST APIs, JSON, API integrations.
Frontend Development: React, Redux Toolkit (RTK), TypeScript, Micro-frontends, Accessibility (A11y).
Cloud & DevOps: AWS (Amazon Connect, Lambda, S3, Redshift, CloudFormation, CDK), Git, CI/CD, Infrastructure as Code.
Professional Skills: Problem solving, technical troubleshooting and debugging skills, stakeholder engagement, technical communication, Agile delivery, teamwork, and continuous learning.
Certifications: AWS Certified Cloud Practitioner or AI Practitioner or Developer – Associate or Solutions Architect – Associate; Agile Fundamentals.