AI Engineer

Must Have

• 3–5 years of software engineering experience, including hands‑on experience building LLM or generative‑AI features.
• Production experience with RAG pipelines, embeddings, and vector databases.
• Demonstrated ability to design, test, and refine prompts and orchestration logic for LLM‑driven workflows.
• Focus on the generative‑AI application layer — distinct from classical model training and MLOps.
• Enthusiasm for working with fast‑moving generative‑AI technologies.

Nice to Have

• Exposure to OCI Generative AI services or other cloud AI platforms.
• Familiarity with agent frameworks and tool integration.
• Experience deploying applications to the cloud, ideally Oracle Cloud Infrastructure (OCI).
• Awareness of responsible‑AI and safety considerations.
• Experience with vector database tuning and retrieval optimization.
• AI or cloud certifications.

Responsibilities

• Develop generative‑AI features and applications using large language models and foundation‑model APIs.
• Implement retrieval‑augmented generation (RAG) pipelines, including document processing, embeddings, and vector search.
• Design, test, and refine prompts and orchestration logic for LLM‑driven workflows.
• Build and integrate agentic components, tool‑calling, and multi‑step flows.
• Integrate AI capabilities into applications and services, including OCI Generative AI services.
• Evaluate model outputs against quality criteria and implement guardrails and validation checks.
• Build evaluation sets and run experiments to compare prompts, models, and configurations.
• Collaborate with senior AI engineers and product teams to deliver working AI features.
• Iterate on solutions based on evaluation results, performance, and user feedback.
• Document AI components, prompts, and integration patterns for maintainability.
• Contribute to internal reusable components and accelerators for generative‑AI delivery.

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related field; equivalent experience accepted.
• Proficiency in Python and experience with LLM frameworks (e.g., LangChain, LlamaIndex) and foundation‑model APIs.
• Working knowledge of RAG, embeddings, and vector databases.
• Understanding of prompt engineering and orchestration techniques.
• Ability to evaluate and improve the quality and reliability of AI outputs.
• Solid general software‑engineering skills, including version control and testing.
• Experience integrating APIs and building application features.