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Open nowPosted 58 days ago

Lead Engineer

medina-talent-group11 open roles

Pay
$135,000 – $150,000 a yearly
Where
Irvine, CA
Work mode
On site
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Your applicationOpen nowLead Engineermedina-talent-group · Irvine, CA

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  2. 4.5%3 days
  3. 12.3%7 days
  4. 19.3%14 days
  5. 35.6%30 days
  6. 57.8%60 days
This job: posted 58 days ago

The posting

Lead Engineer, Enterprise Applications

The Lead Engineer for Enterprise Applications is responsible for driving the design, development, modernization, and lifecycle management of enterprise application portfolios. This role leads end-to-end delivery—from requirements through production—while optimizing applications for scalability, cost-efficiency, and business agility. The position plays a critical role in advancing digital transformation through application rationalization, cloud adoption, and modern engineering practices.

Key Responsibilities

Application Development & Technical Leadership

  • Lead end-to-end delivery of web, mobile, and cloud-native applications using modern frameworks and architectures.
  • Define and govern technical architecture, development standards, and technology stack selection (e.g., React, Node.js, .NET, Java, Python).
  • Drive Agile execution includes sprint planning, backlog refinement, and release management.
  • Incorporate AI-assisted development tools (e.g., code generation, testing automation, documentation) to improve engineering productivity and quality.
  • Evaluate and integrate AI/ML capabilities into applications where appropriate (e.g., intelligent workflows, predictive insights, conversational interfaces).

Application Portfolio Rationalization & Modernization

  • Assess application portfolios to identify redundancy, technical debt, and opportunities for consolidation or modernization.
  • Develop and execute rationalization strategies, including application retirement, consolidation, and migration to cloud or SaaS platforms.
  • Lead modernization initiatives such as microservices adoption, API enablement, containerization, and legacy system transformation.
  • Leverage AI-driven analysis tools to assess application portfolios, identify redundancy, and prioritize modernization opportunities.
  • Identify opportunities to embed AI services into legacy and modernized applications to enhance functionality and user experience.

Team Leadership & Talent Development

  • Manage and allocate engineering resources based on skills, capacity, and strategic priorities.
  • Mentor engineers, conduct code reviews, and promote best practices in modern development and integration patterns.
  • Foster a collaborative, high-performing engineering culture across frontend, backend, DevOps, and integration teams.

Delivery Management & Performance Tracking

  • Track and report on delivery metrics including velocity, defect rates, deployment frequency, and cost optimization.
  • Provide regular updates to stakeholders on progress, risks, technical debt reduction, and modernization ROI.
  • Partner with product owners and business stakeholders to prioritize initiatives aligned with strategic objectives.

Quality, Governance & DevOps Enablement

  • Ensure applications meet standards for performance, security, accessibility, and reliability.
  • Champion CI/CD, automated testing, observability, and DevOps best practices.
  • Participate in architecture governance, change management, and portfolio oversight processes.
  • Implement AI-enabled monitoring and observability solutions for anomaly detection, performance optimization, and incident prediction.
  • Ensure responsible AI practices, including model governance, data privacy, and compliance with enterprise standards.

Cross-Functional Collaboration & Financial Oversight

  • Translate business requirements into scalable technical solutions and modernization roadmaps.
  • Collaborate with vendors and partners supporting application development and transformation initiatives.
  • Manage budgets through forecasting, cost-benefit analysis, and resource optimization.
  • Help define use cases and roadmaps for integrating AI capabilities into business applications.

Required Qualifications

  • Bachelor’s degree in computer science, Software Engineering, or related field (Master’s preferred).
  • 8+ years of software development experience, including 5+ years leading engineering teams and modernization initiatives.
  • Proven experience delivering customer-facing applications and leading enterprise application transformations.
  • Strong proficiency in modern development stacks (JavaScript/TypeScript, React, Node.js, Java/.NET, Python, or similar).
  • Experience with cloud platforms (AWS/Azure), containerization (Docker/Kubernetes), microservices architectures, and legacy modernization.
  • Hands-on experience with Agile and DevOps tools (JIRA, GitHub Actions, Jenkins, Terraform) and application portfolio management practices.
  • Demonstrated success in mentoring engineers and scaling engineering capabilities.
  • Experience integrating AI/ML capabilities or APIs (e.g., NLP, predictive analytics, generative AI services) into enterprise applications.
  • Familiarity with AI-assisted development tools and modern engineering productivity platforms.
  • Understanding data pipelines and integration patterns supporting AI-driven applications.

Preferred Qualifications

  • PMP, AWS Certified Developer, TOGAF, or similar certifications in cloud, architecture, or modernization.
  • Experience with AI/ML platforms or frameworks (e.g., Azure AI, AWS AI/ML services, or similar).
  • Exposure to building or integrating conversational AI, recommendation systems, or intelligent automation solutions.

Key Competencies

  • Strong leadership in team management, mentoring, and cross-functional collaboration.
  • Strategic mindset with experience driving application rationalization and modernization at scale.
  • Ability to communicate effectively with technical teams, business stakeholders, and executive leadership.
  • Data-driven decision-making using KPIs to guide delivery, quality, and portfolio optimization.
  • Ability to operate effectively in fast-paced, transformation-driven environments.
  • Ability to identify and apply AI solutions to improve application functionality, engineering efficiency, and business outcomes.
  • Understanding of responsible AI principles, including governance, explainability, and data security.
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