AI Engineer

Key Responsibilities:

• Develop assigned AI and Generative AI use cases based on approved business requirements and technical designs.
• Build AI assistants, intelligent workflows and prototypes using Large Language Models and enterprise data.
• Prepare, process and analyze structured and unstructured data required for AI solutions.
• Develop and integrate RAG, prompt-engineering and AI-agent capabilities where applicable.
• Build and consume APIs to integrate AI solutions with internal applications and enterprise systems.
• Test and evaluate AI solution accuracy, quality, performance and expected business outcomes.
• Apply source-control, security, data privacy and responsible AI practices during solution development.
• Prepare technical documentation and support deployment, monitoring and production stabilization.

Education:

Bachelor Degree in artificial intelligence, data science, computer science, software engineering, computer engineering or any related field.

Level of Experience:

Fresh Graduate / Entry Level (0-1 year) in a related field. Relevant graduation projects, internships, hackathons or personal AI projects are considered an advantage.

Technical Skills & Knowledge:

Essential:

• Good understanding of AI, Machine Learning and Generative AI concepts.
• Ability to develop AI prototypes and translate business use cases into working solutions.
• Understanding of prompt engineering, LLM APIs and AI solution evaluation.
• Ability to work with structured and unstructured data using Python and SQL.
• Understanding of REST APIs and integration concepts.
• Basic knowledge of secure development, data privacy and responsible AI principles.
• Good analytical, problem-solving, communication and teamwork skills.

Desirable:

• Exposure to RAG, embeddings, vector databases, AI agents and enterprise AI integration.

Certifications & Licensure

Essential:

• Python programming
• Artificial Intelligence / Machine Learning fundamentals
• Generative AI and Large Language Models (LLMs) fundamentals
• SQL and data manipulation
• REST APIs and JSON
• Git / source control

Desirable:

• Microsoft Azure AI Fundamentals (AI-900) or equivalent AI training/certification

Tools & Systems:

Essential:

• Python and common AI/data libraries
• LLM APIs and AI development frameworks
• SQL databases and SQL tools
• Git (GitHub/GitLab/Azure DevOps)
• Postman / Swagger
• Jira / Confluence or equivalent

Desirable:

• Azure AI / Azure OpenAI or equivalent
• Vector databases
• Power BI
• Docker / CI/CD pipelines