Sr Director, Software Engineering

Evolent

1d•$175,000 - $185,000

About The Position

Evolent partners with health plans and providers to achieve better outcomes for people with most complex and costly health conditions. Working across specialties and primary care, we seek to connect the pieces of fragmented health care system and ensure people get the same level of care and compassion we would want for our loved ones. Evolent employees enjoy work/life balance, the flexibility to suit their work to their lives, and autonomy they need to get things done. We believe that people do their best work when they're supported to live their best lives, and when they feel welcome to bring their whole selves to work. That's one reason why diversity and inclusion are core to our business. Join Evolent for the mission. Stay for the culture. What You’ll Be Doing: Sr. Director, AI Platform Engineering Job Summary The Sr. Director of AI Platform Engineering leads the engineering teams supporting Evolent's enterprise AI platform initiatives, with a focus on three capability areas: the document pipeline, event-driven architecture, and agentic user interfaces. The role is accountable for engineering execution in these areas — staffing the teams, delivering the software, and operating it in production — working within the platform direction set with Enterprise Architecture and Product. This is a build-the-team-while-you-build-the-platform role. The leader will stand up and grow multiple engineering teams from an early-stage footprint and deliver capability on a roadmap where later work depends on the foundations landing first. Success requires equal strength in enterprise-scale delivery judgment and in hiring, organizing and developing engineering talent under delivery pressure. Architectural direction and platform design standards are set in partnership with Enterprise Architecture. This role is a primary contributor to and implementer of them rather than their sole owner. The Sr. Director partners closely with Architecture, Product, Data Engineering, Security and the core system engineering teams, and represents the platform engineering organization in executive forums. As a leader in the engineering organization, the Sr. Director fosters a culture of quality, customer obsession, product mindset, and technical excellence. What You Will Be Doing Support New Enterprise-Scale Platform Initiatives Lead the engineering teams delivering the document pipeline, event-driven architecture and agentic user interface capabilities, from build through production support. Support roadmap sequencing with architecture and product partners, so foundational capabilities land ahead of the work that depend on them and hold that sequence when delivery pressure argues for shortcuts. Implement the platform contracts and standards agreed with Enterprise Architecture and contribute to the engineering perspective that shapes them before dependent teams build against them. Drive capabilities from first production consumer to broader enterprise adoption, proving each one at production volume rather than declaring it complete at first release. Partner with the infrastructure and security organizations on the underlying cloud, messaging and hosting foundations the platform depends on. Build and Grow the Teams Stand up and scale the engineering teams behind each capability area, including the team topology, role mix and skill profile each one requires as it moves from early build into production operations. Recruit, hire and onboard engineers and engineering managers at pace, and set the hiring bar for full-stack, platform, integration and UX engineering roles on the platform. Mentor and develop engineering and technical staff, foster leadership and technical growth, and build a bench as the organization grows. Build and maintain a strong engineering culture focused on innovation, collaboration, accountability and continuous improvement, with domain expertise and quality treated as first-class outcomes. Contribute to Technical Direction and Standards Work with executive leadership, enterprise architecture, product management, and other stakeholders to shape the platform's roadmap and long-term strategy and translate it into executable engineering plans. Apply and reinforce enterprise standards for software development, architecture, and quality assurance. Oversee development and delivery of scalable, high-quality software consistent with the approved architecture, with observability, cost telemetry and operational readiness built in from the start. Support the core system engineering teams in defining integration seams, migration paths and fallback handling so existing systems adopt platform capabilities incrementally. Act as a trusted partner to the business by understanding its needs deeply, influencing through technical expertise, and representing the engineering organization in discussions on progress, risks and opportunities. AI-Enabled Delivery Utilize approved AI-assisted engineering tools, and lead teams that use them, to accelerate software development, testing, documentation, troubleshooting and analysis activities. Ensure AI-generated outputs are evaluated and validated for correctness, security, maintainability, compliance, and business appropriateness before they reach production. Continuously identify opportunities to improve engineering productivity and solution quality through AI-enabled workflows and automation. Contribute reusable prompts, workflow templates, engineering playbooks and AI-assisted development patterns that benefit the broader engineering organization. Deliver systems that leverage AI services, APIs, agents and automation capabilities where appropriate, partnering with architecture, security and platform teams on how those capabilities are integrated into enterprise applications. Governance and Compliance Follow and enforce enterprise policies governing AI usage, intellectual property protection, security, privacy, PHI handling and Responsible AI practices across the teams. Ensure no confidential, regulated or protected information is used with unapproved AI services or tooling. Establish peer review and validation processes for AI-assisted solutions and ensure audit visibility and traceability are built into the capabilities the teams deliver.

Requirements

Bachelor's degree in computer science, software engineering, or equivalent experience.
12+ years of software engineering experience, with at least 7+ years in leadership roles managing large, distributed teams.
Proven track record of delivering complex, enterprise-scale software projects on time and within budget, including at least one initiative launched from inception rather than inherited in flight.
Demonstrated experience building engineering teams as part of a new initiative: defining the organization, hiring at pace, and delivering while the team is still forming.
High technical acumen, with the ability to influence technical strategy beyond immediate team scope.
Strong technical expertise in software architecture, API development and system integration, and deep understanding of cloud services and DevOps practices.
Experience using AI-assisted development platforms such as GitHub Copilot, Cursor or similar tools, and familiarity with prompt engineering, AI-assisted testing and AI-enabled software delivery practices.
Experience establishing effective engineering operating models and implementing agile methodologies across teams to drive predictable delivery.
Exceptional leadership and team-building skills, with the ability to lead through change, build a product mindset, and foster a culture of innovation and accountability.
Strong business acumen, with the ability to align technical strategies with organizational goals and priorities.
Outstanding communication, collaboration and stakeholder management skills, with the ability to influence at all levels of the organization and articulate complex technical concepts clearly.
High speed internet over 10 Mbps and, specifically for all call center employees, the ability to plug in directly to the home internet router.

Nice To Haves

Healthcare domain knowledge, particularly medical claims, prior authorization and clinical documentation workflows.
FHIR experience — especially prior-authorization workflows (PAS implementation guide, X12 278 crosswalk) or other healthcare interoperability (HL7v2, CDA).
Knowledge of Domain Driven Design and of identity, access control and observability patterns for AI systems.
Experience integrating AI services, models or agents into production applications, including human-in-the-loop review and approval workflows.
Azure cloud experience (Azure AI services, Azure SQL, Azure Service Bus, containerized deployment).
Experience delivering solutions built on event-driven architectures and enterprise messaging platforms such as Azure Service Bus.
Experience delivering document processing or content pipelines at production scale.
Experience delivering production user interfaces for operational workflows, including review, approval and role-based access.
Healthcare, payer, or other regulated-domain engineering experience

Responsibilities

Lead the engineering teams delivering the document pipeline, event-driven architecture and agentic user interface capabilities, from build through production support.
Support roadmap sequencing with architecture and product partners, so foundational capabilities land ahead of the work that depend on them and hold that sequence when delivery pressure argues for shortcuts.
Implement the platform contracts and standards agreed with Enterprise Architecture and contribute to the engineering perspective that shapes them before dependent teams build against them.
Drive capabilities from first production consumer to broader enterprise adoption, proving each one at production volume rather than declaring it complete at first release.
Partner with the infrastructure and security organizations on the underlying cloud, messaging and hosting foundations the platform depends on.
Stand up and scale the engineering teams behind each capability area, including the team topology, role mix and skill profile each one requires as it moves from early build into production operations.
Recruit, hire and onboard engineers and engineering managers at pace, and set the hiring bar for full-stack, platform, integration and UX engineering roles on the platform.
Mentor and develop engineering and technical staff, foster leadership and technical growth, and build a bench as the organization grows.
Build and maintain a strong engineering culture focused on innovation, collaboration, accountability and continuous improvement, with domain expertise and quality treated as first-class outcomes.
Work with executive leadership, enterprise architecture, product management, and other stakeholders to shape the platform's roadmap and long-term strategy and translate it into executable engineering plans.
Apply and reinforce enterprise standards for software development, architecture, and quality assurance.
Oversee development and delivery of scalable, high-quality software consistent with the approved architecture, with observability, cost telemetry and operational readiness built in from the start.
Support the core system engineering teams in defining integration seams, migration paths and fallback handling so existing systems adopt platform capabilities incrementally.
Act as a trusted partner to the business by understanding its needs deeply, influencing through technical expertise, and representing the engineering organization in discussions on progress, risks and opportunities.
Utilize approved AI-assisted engineering tools, and lead teams that use them, to accelerate software development, testing, documentation, troubleshooting and analysis activities.
Ensure AI-generated outputs are evaluated and validated for correctness, security, maintainability, compliance, and business appropriateness before they reach production.
Continuously identify opportunities to improve engineering productivity and solution quality through AI-enabled workflows and automation.
Contribute reusable prompts, workflow templates, engineering playbooks and AI-assisted development patterns that benefit the broader engineering organization.
Deliver systems that leverage AI services, APIs, agents and automation capabilities where appropriate, partnering with architecture, security and platform teams on how those capabilities are integrated into enterprise applications.
Follow and enforce enterprise policies governing AI usage, intellectual property protection, security, privacy, PHI handling and Responsible AI practices across the teams.
Ensure no confidential, regulated or protected information is used with unapproved AI services or tooling.
Establish peer review and validation processes for AI-assisted solutions and ensure audit visibility and traceability are built into the capabilities the teams deliver.