Software Engineering Senior Advisor - Evernorth

Cigna Healthcare•Tennessee Work at Home, TN

2d•$130,200 - $217,000•Remote

About The Position

The Cigna Group is a global health service company that uses technology and data to improve how people access and experience care. The Software Engineering Senior Advisor is a hands-on technical leader who designs and steers AI, machine learning operations (MLOps), and data engineering solutions at scale, while partnering closely with business, product, and technology stakeholders to align delivery with strategy, risk, and value. This is a full-time position for someone who can both build and lead: you contribute to architecture and implementation, and you influence roadmaps, explain tradeoffs, and drive outcomes across teams that may not share the same technical vocabulary.

Requirements

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) as used in the target environment.
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).

Nice To Haves

Experience in healthcare, insurance, or other highly regulated industries.
Familiarity with HIPAA-aligned practices and enterprise security in data/ML.
Hands-on with LLM/GenAI patterns, guardrails, and evaluation in production settings (as applicable to the team’s strategy).

Responsibilities

AI & machine learning engineering: 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., explain ability, bias awareness, data governance handoffs).
MLOps: 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.
Data Engineering: 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.
Stakeholder Management (core expectation): 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.