AI Applications And Innovation Engineer

AI Engineer

We are at the forefront of developing cutting-edge AI solutions that push the boundaries of machine learning, LLM applications, and agentic AI. Our team builds real-world AI systems and deploys scalable, production-ready solutions across Oracle's enterprise customers.

We are seeking a highly experienced engineer to contribute to the design and deployment of advanced AI systems, including LLM-powered agents, Retrieval-Augmented Generation (RAG) pipelines, and structured AI workflows. As part of our growing team, you will evaluate, prototype, and optimize next-generation agentic AI technologies. This role is ideal for individuals passionate about building and delivering AI solutions that are accurate, reliable, and trusted at enterprise scale. You will play a key role in advancing Oracle's AI strategy—especially in LLMs, Generative AI, and intelligent agent-driven applications.

Responsibilities

AI & LLM System Development

• Design, implement, and deploy AI-driven applications using STOA LLM, Oracle GenAI technologies, and agent evaluation frameworks.
• Build and optimize LLM-powered agents for industry-specific workflows.
• Implement RAG pipelines, structured outputs, and function/tool calling to enable grounded, traceable, and multi-step reasoning.
• Select, version, and optimize foundation models and prompt strategies to meet privacy, latency, cost, and safety objectives.
• Implement guardrails, uncertainty handling, human-in-the-loop processes, and evidence-grounded citations.
• Adapt LLMs to customer domains using prompt engineering, instruction tuning, preference optimization, and domain-specific data.

Evaluation & Quality Assurance

• Evaluate AI methods on industry-relevant datasets to ensure outputs are accurate, reliable, and trustworthy.
• Maintain evaluation harnesses, regression tests, and golden datasets to monitor model performance.
• Conduct systematic error analysis, bias assessments, red-teaming, and active learning to improve quality and close gaps.
• Present insights and findings to internal and external technical audiences.

Data & Platform Engineering

• Integrate search and NLP technologies, including semantic search, conversational search, and summarization.
• Work with Oracle Vector Database and other retrieval systems to optimize AI performance.
• Build and optimize ETL/ELT pipelines and scalable data flows supporting domain adaptation.
• Ensure data security, privacy, and compliance with PHI/PII regulations across all AI workflows.

Production & Cloud-Native Operations

• Productionize AI services with CI/CD pipelines, containerization, orchestration, and autoscaling.
• Instrument traces, metrics, and logs across prompts, retrieval, tools, agents, and model outputs.
• Enforce SLAs through canary and blue-green rollouts with safe rollback procedures.
• Collaborate with cross-functional teams to scale AI offerings across enterprise environments.
• Mentor engineers and foster a culture of engineering excellence.

Software Development & Tooling

• Develop and maintain robust software toolkits in Python, Node.js, and Java to support applied scientists in building, testing, and deploying ML models and agent frameworks.
• Design and implement cloud-based services and APIs for model execution, orchestration, asynchronous communication, and multimodal workflows.
• Produce well-structured sample code and reference implementations, including integrations with LLM APIs, to promote best practices.
• Apply deep knowledge of algorithms, data structures, concurrent programming, and distributed systems to build high-performance and maintainable software.

Collaboration & Technical Leadership

• Partner closely with applied scientists, platform engineers, and cloud infrastructure teams to gather requirements and deliver frictionless ML workflows.
• Produce clear and comprehensive documentation for infrastructure, APIs, designs, troubleshooting, and best practices.
• Participate in code reviews, provide mentorship, incorporate feedback, and help shape engineering standards.
• Conduct systematic error analysis, bias assessments, red-teaming exercises, and active learning to continuously improve quality and close gaps.
• Stay current with emerging trends in AI infrastructure, agent frameworks, HPC systems, and cloud-native technologies; evaluate and integrate them where appropriate.

Qualifications

• Minimum of 12 years of experience in software engineering or AI/ML system development.
• Proven expertise in agentic workflows, LLMOps, RAG architectures, or intelligent automation.
• Strong experience with distributed, high-performance, or cloud-native systems.
• Proficiency in Python and Node.js and familiarity with modern ML/AI libraries.
• Experience with vector databases (Oracle Vector Database preferred).
• Experience deploying AI applications/services on AWS, Azure, GCP, or OCI.
• Demonstrated experience in data collection, annotation, evaluation pipelines, and systematic AI model validation.
• Experience building 0→1 products in fast-paced, ambiguous environments.
• Leadership experience mentoring senior and early-career engineers or scientists.
• Strong communication, problem-solving, and collaboration skills.
• Commitment to staying current with advancements in LLMs, Generative AI, and cloud-native AI/ML technologies.