Senior AI Engineer

Summary

Architect and build generative-AI systems, including RAG and agentic workflows, using LLMs and vector search on Oracle Cloud Infrastructure.

6–10 years of engineering experience, with recent depth in building generative-AI and LLM applications.
Demonstrated track record architecting RAG, agentic, and LLM-powered systems in production.
Experience defining evaluation, guardrails, and safety standards for LLM systems at scale.
Proven ability to mentor AI engineers and review designs, prompts, and implementations.
Strong interest in keeping pace with advances in generative AI.

Experience with OCI Generative AI services and deploying generative AI on Oracle Cloud Infrastructure.
Familiarity with fine-tuning, prompt optimization, and model customization.
Experience in government or regulated environments with data‑residency and privacy constraints.
Knowledge of evaluation tooling and LLM observability platforms.
Experience integrating AI features into enterprise applications.
Awareness of responsible‑AI and governance considerations.
AI or cloud certifications.

Architect generative-AI solutions, including retrieval‑augmented generation (RAG), agentic workflows, and LLM-powered applications.
Lead model selection and evaluation strategy, balancing quality, cost, latency, and data‑residency requirements.
Design and oversee evaluation frameworks, guardrails, and safety measures for AI systems.
Define AI engineering standards and reusable patterns for prompt orchestration, embeddings, retrieval, and vector search.
Design retrieval architectures, chunking and indexing strategies, and grounding approaches for RAG systems.
Optimize generative-AI systems for cost, latency, and reliability at production scale.
Integrate foundation models via APIs, including OCI Generative AI services, into client applications.
Lead the design of agentic systems, including tool use, orchestration, and multi‑step reasoning flows.
Mentor AI engineers and review their designs, prompts, and implementations.
Partner with stakeholders and solution architects to shape AI roadmaps and scope use cases.
Establish observability, evaluation pipelines, and quality metrics for deployed AI features.
Stay current with the rapidly evolving generative‑AI landscape and assess new models, tools, and techniques.

Bachelor's degree in Computer Science, Artificial Intelligence, or a related field; Master's preferred.
Strong Python skills and experience with LLM application frameworks and orchestration (e.g., LangChain, LlamaIndex, or equivalents).
Hands‑on experience with RAG, embeddings, vector databases, and retrieval design.
Experience designing and building agentic systems and tool‑calling workflows.
Experience defining evaluation, guardrails, and safety for LLM systems.
Track record of architecting AI solutions and leading or mentoring engineers.
Understanding of cost and latency optimization for foundation‑model applications.
Ability to communicate technical designs and trade‑offs to technical and business stakeholders.