AIML Engineer

About Oracle Healthcare Data Intelligence

Oracle Healthcare Data Intelligence (HDI) is at the forefront of leveraging data and AI to transform the healthcare and life sciences industries. Our SafetyOne platform uses agentic AI generative models NLP and to automate pharmacovigilance workflows. Were developing multi-agent systems that handle case processing medical coding and safety signal detectionwith appropriate human oversight and regulatory guardrails.

The Opportunity

Were hiring an AI/ML Engineer (IC2) to implement and integrate AI models into SafetyOnes pharmacovigilance platform. Youll work on production AI systems that extract entities from adverse event reports assist with medical coding and causality assessment and generate clinical narratives.

Youll implement LLM-based solutions integrate models into SafetyOne workflows and collaborate with senior data scientists and engineers to deploy AI capabilities at scale. This role offers significant learning opportunities in agentic AI healthcare NLP and regulated software development with clear growth paths toward senior technical roles.

Career Level: IC2

What You Bring

Qualifications

• Bachelors or Masters degree in Computer Science or related technical field with 2 years of experience in AI ML or data-driven solution development.
• Experience building and deploying machine learning models or AI applications
• Familiarity with Python and common ML/NLP libraries
• Experience working with APIs databases and production software systems
• Understanding of software engineering best practices (version control testing documentation)

Technical Skills

Must Have:

• Python programming with experience in ML/data science libraries (scikit-learn pandas numpy matplotlib)
• Familiarity with at least one deep learning framework (PyTorch TensorFlow or similar)
• Basic understanding of NLP concepts: text processing embeddings classification entity extraction
• Experience with SQL and working with both structured and unstructured data
• Understanding of RESTful APIs and integrating ML models into applications
• Familiarity with Git and collaborative development workflows

Highly Valued:

• Exposure to LLMs and prompt engineering
• Hands-on work with RAG or vector databases and semantic search.
• Familiarity with cloud platforms (OCI AWS Azure GCP) and containerization (Docker)
• Comfortable with Linux/Unix environments and command-line tools
• Familiarity with MLOps practices: model deployment monitoring CI/CD for ML
• Experience with healthcare or life sciences data and applications
• Understanding of transformer architectures

Personal Attributes

• Problem-solver with strong analytical and debugging skills
• Collaborative team player who communicates effectively with technical and non-technical stakeholders
• Self-motivated learner eager to grow technical expertise in AI/ML and healthcare domains
• Quality-focused with attention to detail and commitment to production-ready code
• Adaptable and comfortable working in a fast-paced evolving environment

Why Join Us

• Work on challenging real-world AI problems in a regulated domain.
• Build models at production scale for global life sciences organizations.
• Collaborate with experienced engineers scientists and PV experts.
• Access broad learning and career opportunities across Oracle.
• Contribute to systems that directly improve patient safety.

Model Development & Deployment

• Develop LLMs and NLP solutions for pharmacovigilance tasks including entity extraction MedDRA coding assistance and narrative generation
• Build and optimize RAG pipelines for document processing and Q&A systems
• Deploy AI models into SafetyOnes production workflows and user interfaces
• Work with vector databases and semantic search systems to support intelligent retrieval
• Contribute to agentic workflows using modular AI frameworks under guidance

Testing & Quality Assurance

• Contribute to model evaluation frameworks and testing protocols
• Conduct performance analysis and error analysis on model outputs
• Implement guardrails and validation checks to ensure clinical accuracy
• Participate in code reviews and quality assurance processes
• Document model behavior limitations and performance characteristics

Production Deployment & Operations

• Deploy models to Oracle Cloud Infrastructure (OCI) using containerization and MLOps practices
• Monitor model performance in production and address issues as they arise
• Implement logging monitoring and observability for AI systems
• Support model versioning A/B testing and gradual rollouts
• Participate in on-call rotation for production AI systems (as needed)

Collaboration & Learning

• Work closely with senior data scientists software engineers and product managers
• Collaborate with pharmacovigilance domain experts to understand requirements and validate outputs
• Participate in team knowledge sharing code reviews and technical discussions
• Stay current with advances in LLMs RAG systems and healthcare AI

Contribute to technical documentation and best practices

Career Level - IC2