Key Responsibilities
LLM Application Engineering
- Architect and implement scalable LLM-driven applications, leveraging advanced prompt engineering, model customization, and inference optimization.
- Design agentic solutions managing intent routing and workflow orchestration using frameworks such as LangGraph.
- Develop and optimize Retrieval Augmented Generation (RAG) systems, including chunking, embedding selection, vector indexing, and ranking.
- Drive robust prompting patterns for function calling, considering cost, latency, and accuracy trade-offs.
- Integrate with secure microservices, enforce data schemas and metadata lineage, and connect with MCP-compatible tools.
- Implement evaluation datasets, automate regression testing, and monitor quality, cost, and safety of deployed AI systems.
- Establish guardrails for Responsible AI, such as PII handling, access control, and policy enforcement.
Machine Learning & Applied AI Science
- Design, train, and deploy machine learning models develop end-to-end ML pipelines.
- Monitor and optimize model performance, detect data drift, and retrain models as needed.
- Champion best practices in data acquisition, feature engineering, and model curation.
- Lead proof-of-concept AI experiments and publish best practices for the organization.
LLMOps & Platform Engineering
- Manage versioning for prompts, models, and pipelines oversee A/B testing and rollouts.
- Instrument tracing, telemetry, and robust exception handling for agentic workflows.
- Steer integration and interoperability with Oracle Fusion products and cloud infrastructure.
Evaluation & Quality
- Develop and maintain rigorous evaluation frameworks, key performance indicators, and benchmarking strategies for model performance, safety, and business value alignment.
Responsible AI & Security
- Serve as a technical authority to ensure privacy, model governance, Responsible AI, and operational security throughout the ML lifecycle.
Conversational UX & Domain Leadership
- Define standards and mentor engineers for multi-turn dialog systems and conversational interfaces.
- Act as a domain expert, influencing technical direction, reviewing code, and fostering a culture of innovation and knowledge sharing in agile teams.
Essential Skills
- Advanced expertise in Python (and/or Java) and modern ML/AI libraries (TensorFlow, PyTorch, etc.).
- Deep experience with large language models (OpenAI, Google, proprietary/foundation models), prompt engineering, and custom model deployment.
- Mastery of cloud enterprise ecosystems (with Oracle Cloud and Fusion preferable) strong grasp of enterprise-class observability, governance, and security.
- Demonstrated skills in architecting scalable AI/ML pipelines and deploying production-ready enterprise applications.
- Strong background in conversational AI and NLP, including dialog management and customer-centric natural language experiences.
- Proven ability to enforce best practices in responsible, safe, and secure AI development.
- Exceptional leadership, mentoring, and technical communication skills.
- Track record of influencing technical direction and driving adoption of advanced AI techniques in multidisciplinary teams.
Qualifications
Required:
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related quantitative discipline
- 8+ years of industry experience in software or AI/ML development, including at least 3 years in LLMs or deep learning.
- Demonstrable experience with modern AI/ML frameworks and deploying models in cloud production environments.
- Experience with enterprise-level data pipelines, versioning, CI/CD, and ML/MLOps tooling (e.g., OCI Data Science, Kubeflow, MLflow).
- Hands-on expertise integrating AI with microservices, enterprise APIs, and secure data architectures.
- Deep understanding of Responsible AI principles, privacy, and model governance.
Preferred:
- Experience with Oracle Cloud Infrastructure (OCI) and Oracle Fusion applications.
- Prior design and implementation of agentic workflows and Retrieval Augmented Generation (RAG) systems.
- Strong publication record, patents, or industry contributions in applied AI.
- Experience with productionizing multi-turn conversational UX and building enterprise AI solutions at scale.
- Active participation in AI/ML research or open-source communities.
Why Join Our Team
- Shape the future of Oracle's AI solutions driving global business value.
- Work on impactful, immediate solutions incorporated into Oracle's product suite.
- Lead transformative, complex AI initiatives in a highly supportive and innovative environment.
- Collaborate with top AI engineers and scientists in a culture that encourages continuous learning and growth.
