Senior AI Engineer

Must Have

• 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.

Nice to Have

• 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.