About the role
AI summarisedLead internal and offshore/vendor engineering teams in the end-to-end design, development, and delivery of enterprise applications, microservices, cloud-native platforms, and AI/ML initiatives. Drive application modernization efforts while ensuring high standards of scalability, performance, security, and operational excellence.
IndustrialOnsite
Key Responsibilities
- Lead end-to-end delivery of microservices-based applications, APIs, backend services, and cloud solutions.
- Drive application modernization initiatives, including assessment of legacy systems, target-state architecture definition, and phased migration execution.
- Oversee solution design, development, deployment, release management, production support, and continuous improvement.
- Guide development across technologies including Java, Python, .NET, Spring Boot, TypeScript, React, and Angular.
- Drive engineering best practices across Kubernetes, Azure, AWS, DevOps, DevSecOps, and CI/CD.
- Support the design and delivery of AI/ML-enabled applications, including ML model integration and intelligent workflow development.
- Manage vendor delivery, distributed team coordination, technical governance, and engineering quality assurance.
- Serve as a key liaison between business stakeholders, architects, product owners, and technical teams.
- Lead, mentor, and manage team members, setting objectives, monitoring performance, and providing career guidance.
Requirements
- Bachelor’s degree or above in Computer Science, Software Engineering, IT, or a related field.
- 10+ years of experience in software development.
- At least 5 years in a leadership role such as Software Development Manager or Engineering Manager.
- Strong hands-on background in architecture design for high-performance distributed systems and cloud-native platforms.
- Proven experience managing both in-house and offshore/vendor development teams.
- Demonstrated success modernizing legacy or monolithic applications into scalable, cloud-ready architectures.
- Strong proficiency with technologies including Java, Python, Spring Boot, .NET/C#, TypeScript, React, Angular, Kubernetes, Azure, and AWS.
- Good understanding of software architecture, API design, distributed systems, security, and scalability.
- Exposure to AI/ML application delivery lifecycle and data platform concepts (pipelines, governance).