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AI Engineer
Job Description
Must Have
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.
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Enthusiasm for working with fast-moving generative-AI technologies.
- 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.
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AI or cloud certifications.
- 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.
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Contribute to internal reusable components and accelerators for generative-AI delivery.
- 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.
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