Senior Principal Machine Learning Engineer

Established Search is recruiting for a healthcare technology business that uses digital services and AI to help people navigate care and make informed healthcare decisions. The Senior Principal Machine Learning Engineer will set technical direction and deliver hands-on engineering across data platforms, machine learning systems and generative AI, while influencing and mentoring teams.

Responsibilities:

• Own the technical architecture and roadmap across data infrastructure, ML systems and generative AI capabilities
• Identify and resolve systemic architectural issues across the Data and AI stack
• Guide complex initiatives spanning multiple teams and systems, communicating risks and key decisions to leadership
• Work with stakeholders to translate business requirements into technical solutions
• Design fault-tolerant distributed pipelines supporting batch and streaming workloads
• Implement checkpointing, replay capabilities and schema evolution
• Improve warehouse and lakehouse performance through partitioning, query optimisation and workload isolation
• Establish end-to-end data lineage, data quality service levels and automated data contract enforcement
• Build reliable systems across model training, evaluation, deployment and production monitoring
• Implement drift detection, reproducible deployments and rollback strategies
• Develop and improve LLM workflows, including retrieval-augmented generation, fine-tuning and prompt engineering
• Evaluate retrieval quality and groundedness, with automated regression checks
• Contribute directly to production code while guiding high-level system design
• Embed governance, quality, security and observability into platform development
• Raise engineering standards through code reviews, design feedback and coaching
• Mentor engineers and influence technical decisions across teams

Requirements:

• At least 12 years of professional software or data engineering experience, including substantial experience at Principal level or above
• Expert knowledge of distributed processing, batch and streaming pipelines, and warehouse or lakehouse architecture
• Experience with technologies such as Spark or Flink
• Strong ML engineering experience across the full model lifecycle
• Practical experience with modern LLM systems, including RAG, fine-tuning and prompt engineering
• Strong Python and SQL skills
• Experience building cloud-native platforms using AWS, GCP or Azure
• Experience optimising data platform performance and implementing governance, lineage and observability
• A track record of leading complex technical initiatives through influence and collaboration
• Clear written and verbal communication with technical and non-technical stakeholders
• Familiarity with Databricks, dbt, Kafka, MLflow or equivalent tools
• Experience in healthcare, employee benefits or another regulated industry
• Effective use of generative AI tools to improve engineering productivity and quality
• Experience defining governance and observability frameworks at platform scale
• Experience mentoring senior engineers or leading technical communities