Senior Software Engineering Advisor

Responsibilities

• Design and review end-to-end AI/ML solutions: model lifecycle, feature patterns, serving patterns, and integration with business applications
• Partner with data science and product teams to turn prototypes into production systems with clear ownership, monitoring, and maintenance paths
• Stay current on responsible AI considerations relevant to healthcare and regulated data (e.g., explainability, bias awareness, data governance handoffs)
• Define and implement MLOps practices: CI/CD for models, registries, deployment strategies, A/B or shadow deployments where appropriate, and rollback
• Ensure observability for models and pipelines: performance drift, data quality, latency, and business-aligned KPIs
• Work with security and platform teams on infrastructure, access patterns, and compliance in ML workflows
• Lead design of reliable, scalable data pipelines and platforms that feed training, batch scoring, and real-time inference
• Promote data quality, lineage, and contract thinking so ML and analytics consumers trust what they build on
• Balance build vs. buy (cloud services, feature stores, orchestration) with total cost, maintainability, and team skills
• Translate between business goals and technical options: scope, phasing, risk, and “good enough for now” vs. “must be right first”
• Facilitate planning with product owners, legal/compliance, architecture, and operations; document decisions and follow through
• Mentor engineers and data practitioners; represent the engineering viewpoint in cross-functional forums without losing sight of delivery

• Bachelor’s degree in Computer Science, Engineering, or related field (or equivalent practical experience)
• Typically 8+ years of software/data engineering experience, including substantial work in at least two of: ML/AI systems, MLOps, or data engineering at scale
• Demonstrated experience leading without direct authority: influencing architecture, roadmaps, and multiple teams
• Strong proficiency in Python and/or other languages common in data/ML stacks (e.g., Scala, Java)
• Cloud platforms (e.g., AWS, Azure, GCP) and experience with containerization, orchestration (e.g., Kubernetes, Airflow, similar), and CI/CD
• Data: SQL, batch/streaming patterns, and familiarity with data lakes/warehouses and ELT/ETL
• ML lifecycle: model packaging, serving (batch and/or online), experiment tracking, and production monitoring
• Excellent written and verbal communication; ability to run workshops, design reviews, and executive-ready summaries of options and tradeoffs
• Track record of stakeholder alignment on complex, cross-team initiatives in regulated or enterprise environments (healthcare/insurance a plus)

• AI solutions
• ML solutions
• MLOps
• CI/CD
• Python
• Scala
• Java
• SQL
• data engineering
• model lifecycle

• leadership
• communication
• mentoring
• influencing
• stakeholder alignment
• planning
• documentation
• cross-functional collaboration
• problem-solving
• decision-making

• Bachelor’s degree in Computer Science
• Bachelor’s degree in Engineering
• related field degree