Senior AI/ML Engineer
Indexed description
Careers with Optum offer flexible work arrangements and individuals who live and work in the Republic of Ireland will have the opportunity to split their monthly work hours between our Dublin or Letterkenny office and telecommuting from a home-based office in a hybrid work model.
What You Will Do
- Design, build, and deploy generative AI solutions - including retrieval-augmented generation, prompt and context engineering, orchestration, and agentic workflows - grounded in enterprise healthcare data
- Develop and productionize classical machine learning models, covering feature engineering, training, tuning, validation, and inference
- Build the evaluation harnesses, test sets, and quality metrics used to measure model and GenAI system performance, including accuracy, groundedness, latency, and cost
- Implement MLOps practices such as experiment tracking, model registries, automated pipelines, CI/CD, and monitoring for drift, quality, and reliability in production
- Work with data engineering to shape the datasets, semantic layers, and retrieval indexes that AI and ML workloads depend on
- Partner with product, business, architecture, security, and governance stakeholders to turn use cases into workable solutions, and to explain what the models can and cannot do
- Apply responsible AI practices, including privacy, safety, bias assessment, human-in-the-loop review, and traceability for sensitive and regulated data
- Contribute to team engineering standards through code review, documentation, and reusable components, and share knowledge with peers
What You Will Bring
- Bachelor's degree in a relevant field, or equivalent professional experience
- Proven experience building and deploying machine learning or AI solutions into production within public cloud environments
- Proven hands-on experience developing Generative AI applications using large language models, including prompt engineering, retrieval-augmented generation (RAG), embeddings, vector search, and orchestration frameworks
- Demonstrated solid understanding of classical machine learning, including feature engineering, model selection, training, tuning, and validation
- Demonstrated advanced proficiency in Python and SQL, with experience using machine learning and data libraries
- Proven experience with MLOps tools and practices, including experiment tracking, model versioning, automated deployment pipelines, and production monitoring
- Demonstrated solid understanding of software engineering practices, including automated testing, version control, CI/CD, and production support
- Demonstrated working knowledge of data preparation and data pipeline concepts, with experience working with large datasets
- Demonstrated understanding of cloud security, identity and access management, and privacy principles for sensitive data
- Proven ability to collaborate with technical and non-technical stakeholders and communicate AI and machine learning concepts, trade-offs, and limitations effectively
- Proven hands-on experience with Azure AI services, Databricks, and Snowflake
- Proven experience working with MLflow, Apache Spark, and deep learning frameworks such as PyTorch or TensorFlow
- Proven experience evaluating machine learning and Generative AI system quality using offline and online methodologies and leveraging findings to drive continuous improvement
- Proven experience working with vector databases, hybrid search solutions, and retrieval optimization techniques
- Proven experience building agentic AI, tool-using AI systems, or conversational analytics solutions leveraging enterprise data
- Proven experience with model fine-tuning, model adaptation techniques, and assessing their applicability to business requirements
- Proven experience working with containerization, infrastructure as code, and technologies such as Docker, Kubernetes, or Terraform
- Proven experience applying responsible AI, governance, and security controls to sensitive or regulated data environments
- Proven experience within the healthcare industry
- Opportunities for professional development
- Inclusive and supportive team culture
- Key benefits: Private health insurance, wellness programs, matching pension contribution, lunch provided by the company, training opportunities, employee donations matching and others
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