Job Description

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., Lang Chain, Llama Index) 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.