Software Development Manager

Position Summary

We are seeking an experienced and results-driven Software Development Manager to lead our Application Engineering and AI Engineering teams. This role combines technical leadership, people management, strategic planning, and delivery oversight to build innovative software products, AI-powered solutions, and enterprise applications that support business objectives.

The Software Development Manager will have direct people-management responsibility for Application Engineering and AI Engineering personnel, including hiring, performance management, talent development, workforce planning, and employee engagement. This role will lead onshore and offshore engineering teams, drive software development best practices, and ensure the successful delivery of scalable, secure, and high-quality technology solutions. The position partners closely with Data Engineering, Cloud Engineering, Business Intelligence, Infrastructure, Security, Enterprise Architecture, and business stakeholders to deliver integrated enterprise solutions.

The Software Development Manager will also support technology due diligence and post-acquisition integration initiatives, helping assess, integrate, and optimize acquired software platforms, engineering teams, and technology capabilities. This includes collaborating with cross-functional stakeholders to develop integration roadmaps, identify modernization opportunities, and drive the successful adoption of acquired technologies into the enterprise ecosystem.

Key Responsibilities

Leadership & Team Management

• Provide direct leadership and management for Application Engineering and AI Engineering team members, including onshore and offshore employees and team leads.
• Conduct hiring, onboarding, performance reviews, compensation recommendations, coaching, career development, succession planning, and employee engagement activities.
• Lead, mentor, and develop high-performing engineering teams while fostering a culture of accountability, innovation, and continuous improvement. Foster a collaborative, high-performance engineering culture focused on innovation, accountability, and continuous improvement.
• Promote effective communication and collaboration across globally distributed teams.

Engineering Delivery & Operations

• Manage the end-to-end software development lifecycle for enterprise applications and AI-driven solutions.
• Establish engineering priorities, resource plans, and delivery roadmaps aligned with business objectives.
• Ensure adherence to architecture standards, coding practices, security requirements, quality standards, and operational excellence.
• Drive Agile delivery practices, release management, and continuous improvement initiatives.
• Monitor team performance, delivery metrics, application reliability, and operational effectiveness.
• Manage engineering budgets, vendor relationships, and offshore delivery partnerships.

Application & AI Engineering Leadership

• Oversee the design, development, deployment, and support of enterprise applications and AI-enabled solutions.
• Drive modernization, cloud adoption, automation, and application performance improvements.
• Lead AI/ML, Generative AI, and data-driven solution development while establishing governance, MLOps, and responsible AI practices.
• Partner with Data Engineering and Business Intelligence teams to operationalize AI solutions and enable business adoption of AI-driven insights.

Strategy & Cross-Functional Collaboration

• Partner with technology and business leaders to define engineering roadmaps and technology strategies.
• Evaluate emerging technologies and identify opportunities to improve efficiency, scalability, and business value.
• Collaborate with Data Engineering, Cloud Engineering, Business Intelligence, Infrastructure, Security, and Architecture teams to deliver integrated solutions.
• Drive cross-functional planning and execution for initiatives spanning multiple technology teams.
• Support technology due diligence and post-acquisition integration efforts, including assessment, planning, and integration of acquired applications, platforms, and engineering teams.

Operational Excellence

• Establish KPIs and metrics for engineering effectiveness, quality, reliability, and customer satisfaction.
• Ensure compliance with security, governance, and regulatory requirements.
• Monitor and optimize offshore team performance, utilization, and delivery outcomes.
• Drive continuous improvement through automation, DevOps, and engineering best practices.

Required Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Information Technology, or related field.
• 8 years of software development experience with progressive technical leadership responsibilities.
• 3 years of experience managing software engineering teams.
• Experience leading enterprise application development and AI/ML initiatives.
• Experience managing distributed, offshore, or global engineering teams.
• Strong understanding of software architecture, APIs, microservices, cloud platforms, and modern development frameworks.
• Experience with Agile methodologies, DevOps practices, and software delivery management.
• Strong communication, stakeholder management, and leadership skills.
• Experience collaborating with Data Engineering, Cloud Engineering, Business Intelligence, Infrastructure, and Security teams.

Preferred Qualifications

• Master's degree in Computer Science, Engineering, Data Science, or related field.
• Experience with Generative AI, LLMs, RAG, and AI platforms.
• Experience with Azure, AWS, or Google Cloud.
• Familiarity with MLOps, CI/CD, containerization, and cloud-native architectures.
• Relevant certifications in Cloud, AI/ML, Agile, or Project Management.

Technical Competencies

Application Engineering

• .NET, Java, Python, Node.js, or similar technologies
• APIs, microservices, and cloud-native architectures
• SQL and NoSQL databases
• DevOps and CI/CD practices

AI Engineering

• Machine Learning and Generative AI technologies
• MLOps and model deployment pipelines
• AI governance and monitoring
• Data and analytics platforms

Cross-Functional Technology Knowledge

• Cloud platforms (Azure, AWS, GCP)
• Data platforms and analytics ecosystems
• Business Intelligence and reporting solutions
• Security, governance, and enterprise integration patterns

Success Metrics

• Delivery of high-quality software and AI solutions on time and within scope.
• Improved engineering productivity, quality, and team engagement.
• Increased adoption and business value from AI-enabled solutions.
• Reliable, secure, and scalable application platforms.
• Effective collaboration across engineering disciplines and offshore teams.
• Achievement of strategic technology and business objectives.