Manager, Software Engineering

Evolent

About The Position

Evolent is seeking a Manager, Software Engineering for a player-coach role leading day-to-day work of a software engineering team while contributing to delivery. The position provides technical guidance and mentorship, collaborates with stakeholders on requirements and roadmaps, and oversees the design, development, and deployment of scalable software solutions. The manager promotes agile practices, coordinates with cross-functional teams, supports system reliability, and reports project status to leadership and stakeholders. The role applies AI-assisted engineering across the delivery lifecycle, including requirements analysis, development, testing, debugging, documentation, and operational monitoring. It includes iterating on solution and architecture design with AI, validating AI-generated work for correctness, security, maintainability, compliance, and business suitability, and supporting governed data platforms and datasets for machine learning, analytics, and generative AI. The position also contributes reusable prompts, tools, playbooks, and development patterns, while ensuring AI use follows enterprise policies for intellectual property, security, privacy, PHI, and Responsible AI. Evolent describes its culture as emphasizing work/life balance, flexibility, autonomy, diversity, and inclusion. Employees must have high-speed home internet over 10 Mbps. The hiring process includes identity verification, background checks, and in-person I-9 verification; final interviews may require onsite attendance. AI tools are prohibited during interviews.

Requirements

Bachelor's degree in Computer Science, Software Engineering, or a related field.
At least 5 years of software development experience, including at least 1 year in a team leadership role.
Proficiency in one or more programming languages, such as C# or Python, and relevant frameworks.
Experience with modern software development methodologies, including Agile or Scrum.
Knowledge of cloud platforms such as Azure, AWS, or GCP, and experience with data engineering technologies such as Spark, Databricks, or Microsoft Fabric.
Knowledge and experience with modern data engineering patterns, such as Data Vault, dimensional modeling, data products, lakehouse architecture, feature flags, event-driven systems, and DAGs.
Strong problem-solving and analytical skills, with attention to scalability and performance.
Excellent communication and interpersonal skills and the ability to collaborate across teams.
Familiarity with version control systems such as Git and CI/CD tools for deployment automation.
A proactive leadership approach that fosters a positive and inclusive team environment.
Demonstrated experience using AI-assisted development platforms such as GitHub Copilot, Cursor, Claude Code, or similar tools to deliver production work.
Strong prompt engineering skills, with experience in AI-assisted testing and AI-enabled delivery practices.
Experience with machine learning data pipelines, vector search technologies, document processing pipelines, or AI data preparation techniques.
Working knowledge of data governance, lineage, quality, and observability practices supporting AI-enabled solutions.

Responsibilities

Assist in managing the software engineering team's day-to-day activities and deliver alongside the team, aligning work with project goals and timelines.
Provide technical guidance and mentorship to team members.
Collaborate with stakeholders to gather requirements, define project scope, and create technical roadmaps.
Oversee the design, development, and deployment of scalable software solutions.
Conduct code reviews to ensure best practices, quality standards, and coding guidelines are followed.
Troubleshoot and resolve software issues to maintain system performance and reliability.
Promote agile methodologies and contribute to continuous process improvement.
Coordinate with cross-functional teams to integrate software components.
Prepare and present project status updates to leadership and stakeholders.
Apply AI-assisted engineering across requirements analysis, pipeline and code development, testing, debugging, documentation, and operational monitoring.
Use AI iteratively in solution and architecture design, evaluate alternatives, revise architecture, and produce design artifacts for review.
Set standards for validating AI-generated outputs and review other engineers' AI-assisted work for correctness, security, maintainability, compliance, and business appropriateness.
Design and support data platforms and governed datasets for machine learning, analytics, and generative AI workloads.
Contribute reusable prompts, skills, agents, engineering playbooks, and AI-assisted development patterns.
Ensure AI usage complies with enterprise policies for intellectual property protection, security, privacy, PHI handling, and Responsible AI standards.