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 and productionize LLM-powered solutions using Azure OpenAI - including retrieval-augmented generation, prompt and context engineering, and agentic workflows grounded in enterprise healthcare data
- Designing agent instructions, tools and orchestration flows
- Building guardrails, validation and fallback behaviour
- Integrating agents with APIs, applications and enterprise services
- Troubleshooting retrieval, semantic-model, prompt and tool-selection issues
- Build applied AI and generative AI solutions that support data governance, quality, and compliance across the data lifecycle
- Orchestrate and schedule data workflows using tools such as Apache Airflow and Azure Data Factory, ensuring reliability, observability, and cost efficiency
- Implement data governance, data quality, lineage, schema drift detection, and access-control/masking practices to keep sensitive and regulated data secure and compliant
- Develop evaluation, throttling, and reliability patterns for LLM systems - measuring accuracy, groundedness, cost, and latency in production
- Partner with data science, product, architecture, security, and governance stakeholders to turn use cases into workable, well-documented solutions
- Apply responsible data and AI practices, including privacy, safety, bias awareness, 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 operating production data pipelines and platforms within public cloud environments
- Demonstrated advanced proficiency in SQL and Python, with experience using data engineering and data processing libraries
- Proven hands-on experience with modern cloud data platforms such as Snowflake and Databricks
- Proven hands-on experience developing Generative AI applications using large language models, including Azure OpenAI, LLM gateways, prompt engineering, retrieval-augmented generation (RAG), embeddings, and vector search
- Proven hands-on experience designing, deploying, and supporting production large language model (LLM) and agentic AI solutions, including retrieval, tool integration, evaluation, monitoring, and failure handling
- Proven experience integrating LLMs into Databricks, Snowflake, or similar data platforms to support production use cases
- Proven experience designing semantic models and business-friendly data abstractions to support natural language analytics and AI agents
- Demonstrated working knowledge of data modeling, data preparation, and data pipeline concepts, with experience working with large datasets
- Proven experience with DataOps practices, including version control, automated testing, CI/CD, and production monitoring
- Demonstrated understanding of cloud security, identity and access management, data governance, and privacy principles for sensitive data
- Proven ability to collaborate with technical and non-technical stakeholders and communicate data and AI concepts, trade-offs, and limitations effectively
- Proven experience developing AI solutions using Snowflake Cortex, Cortex Search, Cortex Analyst, semantic models, or similar AI platforms
- Proven experience with data governance tooling, data cataloging, lineage, schema drift detection, and data masking and classification
- Proven experience with Snowflake performance tuning and cost optimization, including clustering, warehouse sizing, and query profiling
- Demonstrated solid understanding of classical machine learning, including feature engineering, model selection, training, tuning, and validation
- Proven experience applying responsible AI, governance, and security controls within 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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