Software Engineering Senior Advisor - Evernorth

📋 Description

• • Design and review end-to-end AI/ML solutions including model lifecycle, feature patterns, serving patterns, and integration with business applications.
• • Partner with data science and product teams to transition AI/ML prototypes into production systems with clear ownership, monitoring, and maintenance paths.
• • Stay current on responsible AI considerations in healthcare and regulated data environments, including explainability, bias awareness, and data governance handoffs.
• • Define and implement MLOps practices such as CI/CD for models, model registries, deployment strategies (including A/B and shadow deployments), and rollback mechanisms.
• • Ensure observability for ML models and pipelines by monitoring performance drift, data quality, latency, and business-aligned KPIs.
• • Collaborate with security and platform teams to enforce infrastructure standards, access controls, and compliance requirements within ML workflows.
• • Lead the design of scalable and reliable data pipelines and platforms that support training, batch scoring, and real-time inference.
• • Promote data quality, lineage, and contract-based thinking to ensure ML and analytics consumers trust their data inputs and outputs.
• • Evaluate and balance build versus buy decisions for cloud services, feature stores, and orchestration tools based on total cost, maintainability, and team skill sets.
• • Translate business goals into technical options, clearly articulating scope, phasing, risk, and tradeoffs between "good enough for now" and "must be right first."
• • Facilitate planning sessions with product owners, legal/compliance, architecture, and operations teams; document decisions and ensure follow-through.
• • Mentor engineers and data practitioners, representing the engineering perspective in cross-functional forums while maintaining focus on delivery.
• • Maintain strong proficiency in Python and/or other languages common in data/ML stacks such as Scala or Java as used in the target environment.
• • Apply expertise with cloud platforms (AWS, Azure, GCP) including containerization, orchestration tools (Kubernetes, Airflow), and CI/CD pipelines.
• • Design and manage data systems using SQL, batch/streaming patterns, and familiarity with data lakes, data warehouses, and ELT/ETL processes.
• • Implement ML lifecycle components including model packaging, serving (batch and online), experiment tracking, and production monitoring.
• • Communicate effectively through written and verbal channels, conducting workshops, design reviews, and delivering executive-ready summaries of technical tradeoffs.
• • Align cross-functional stakeholders on complex initiatives in regulated or enterprise environments, with preference for experience in healthcare or insurance.
• • Ensure internet connectivity for remote work meets minimum requirements of 10Mbps download and 5Mbps upload via cable broadband or fiber optic service provider.