Role Overview
ShipDelight is looking for a hands-on Manager – Engineering candidate who can lead engineering teams through the transition from legacy systems to modern , scalable , data-driven and AI-enabled engineering practices.
This role requires a strong balance of technical depth , delivery ownership , team leadership and business-domain understanding. The ideal candidate should be comfortable working with existing systems , identifying modernization opportunities , improving engineering quality and enabling teams to adopt newer development practices without disrupting business continuity.
The Manager – Engineering will work closely with product, operations, business, DevOps, data, QA, and leadership teams to translate business needs into reliable technology outcomes. The role is especially suited for someone who can understand domain workflows deeply , analyze system and operational data , remain hands-on when needed and guide teams toward AI-assisted , automation-led engineering.
The ideal candidate should preferably come from a leading IT Services organization, bringing strong engineering discipline, client-facing experience and the ability to operate in fast-paced product environments.
Key Responsibilities
- Own end-to-end engineering delivery across platform , product and business-critical technology initiatives.
- Lead the transition of legacy applications , workflows and engineering practices toward modern architecture , scalable systems , cloud-ready infrastructure , automation and improved development standards.
- Work closely with business and operations teams to understand domain workflows , user pain points , operational dependencies and system-level impact before driving technical solutions.
- Bring a data-oriented approach to engineering decisions by analyzing system metrics , operational data, product usage patterns , incident trends , release performance and delivery bottlenecks.
- Drive engineering execution across backend , frontend , database , QA , DevOps and integration workstreams while maintaining clarity on scope , priorities , dependencies and timelines.
- Stay hands-on with architecture reviews , technical problem-solving , database design , API design , code quality , performance optimization and production issue analysis.
- Identify areas where AI-assisted engineering , automation and developer productivity tools can improve code quality , delivery , testing , documentation , remediation and operational efficiency.
- Partner with DevOps and infrastructure teams to strengthen deployment reliability , environment stability , observability , rollback readiness and production support processes.
- Build a strong execution culture using tools such as Jira , GitHub , CI/CD pipelines , issue tracking systems , dashboards and engineering metrics.
- Guide teams in breaking down complex technical and business problems into clear engineering plans , milestones , ownership areas and measurable outcomes.
- Support hiring , performance feedback , capability building and team development while maintaining high engineering standards.
Required Skills and Experience
- 6–8 years of software engineering experience in an IT Services environment building ground-up platforms with minimum 2 years of experience in a client-facing requirements gathering and engineering management role, preferably working with international customers or global delivery teams.
- Strong experience working on business-critical platforms – preferably involving legacy systems , workflow-heavy applications , integrations , data-driven operations or enterprise systems.
- Hands-on understanding of backend systems , databases , APIs , web platforms , cloud infrastructure , release management and production support
- Experience leading engineering teams across multiple functions such as backend , frontend , QA , DevOps , data and platform engineering.
- Hands-on experience in Python-based application development in addition to modern backend engineering technologies.
- Ability to understand existing systems , identify architectural , fix operational gaps and create practical modernization roadmaps.
- Strong database and data-analysis mindset include the ability to interpret operational data , system metrics , logs , reports and business process data to make better engineering decisions.
- Good understanding of CI/CD , version control , automated testing , environment management , deployment workflows , monitoring and incident management.
- Exposure to AI-assisted development tools , automation workflows , code quality platforms and AI-enabled engineering practices.
- Ability to work closely with business, operations, product teams and clients to translate business problems into practical engineering solutions.
Behavioral Competencies
- High influencing ability across functions and levels (horizontal and vertical)
- Excellent interpersonal and stakeholder management skills
- Strong ownership and execution orientation
- High learning agility with the ability to quickly adapt to new technologies and business domains
- Collaborative leadership style with the ability to build credibility across engineering, product and business teams
What Success Looks Like
- Legacy systems become more stable , maintainable , observable and easier to enhance.
- Engineering teams move toward cleaner architecture , better documentation , stronger code quality and more predictable releases.
- Business and operations teams experience fewer technology bottlenecks and better alignment with engineering.
- Data becomes a regular part of engineering planning , prioritization , incident analysis and product improvement.
- AI-assisted engineering tools are adopted responsibly to improve productivity without compromising quality , security and accountability.
- The engineering team develops stronger ownership , better technical judgment and deeper understanding of the business domain.
