Manager R&D Services

Position: 

As Manager - R&D Services, you will support senior management to sustain, build, and manage the R&D Services service line, covering Software Development and Software Quality Assurance.  

You will serve R&D Business Unit and Department Managers across all divisions supported by the Resource Centre and will dedicatedly contribute to AI Enablement within R&D - providing source guidance to management and overseeing AI-related consulting engagements and initiatives. 

Duties & Responsibilities: 

Service Line Management  

• Support senior leadership in managing and growing the R&D Services service line, ensuring quality, consistency, and scalability across Development and QA. 

• Perform or lead the technical skills assessment component of recruitment, operating hands-on for specialized or emerging skill areas and providing oversight for standard hiring streams. 

• Work with divisions to understand current and future capability needs, shaping hiring, onboarding, and upskilling strategies. 

• Drive service quality, best practices, and delivery discipline across the service line. 

AI Enablement & Advisory  

• Being part of the AI Enablement for the R&D Service Line, help divisions embed AI practically and responsibly within development and QA workflows. 

• Provide practical guidance, plan and advisory support to management and divisions such as where AI can meaningfully improve productivity, quality, or decision making, where any particular tool or practice may introduce risk or limited value, and how adoption should be sequenced and governed. 

• Demonstrate hands-on familiarity with modern AI-assisted, AI-powered, and AI-autonomous SDLC engineering approaches, with practical experience using tools such as Cursor, Claude Code, and GitHub Copilot, and models such as Claude Sonnet, Claude Opus, and GPT etc. 

• Experience should span development, testing, deployment, code generation, refactoring, debugging, documentation, analysis etc. within SDLC with agentic AI, at both conceptual and applied levels. 

• Experience of ensuring AI is adopted as an engineering accelerator, not as ad‑hoc tooling or uncontrolled experimentation. 

AI Consulting & Engagement 

• Oversee AI-focused engagements, including internal initiatives within Admin team, and external for divisions consulting projects where applicable. 

• Shape consulting engagements by helping define problem statements, guiding solution approaches, and assessing feasibility, risks, and outcomes. 

• Work with execution teams and specialists to ensure solutions meet requirements, outcomes are measurable, and delivery discipline is optimized and maintained. 

• Act as the accountable owner for AI consulting outcomes from an R&D perspective. 

Internal Enablement & Capability Building 

• Support and contribute to Contour-wide AI and engineering training initiatives, guiding content direction, contextualizing learning for Dev and QA teams, and ensuring learning translates into real productivity gains. 

• Evaluate emerging tools, platforms, and patterns - focusing on practical applicability rather than experimentation. 

• Help establish reference architecture, usage guidelines, and best practices for AI-SDLC engineering. 

People Management & Leadership 

• Perform core people management responsibilities to ensure employee growth, retention, and engagement. 

• Mentor and coach team managers, leaders, and senior engineers, particularly in evolving engineering practices and AI-aware delivery models. 

• Partner with HR and L&D on career development paths, capability planning, and leadership readiness. 

Knowledge Sharing & Service Maturity 

• Stay current with industry trends in software engineering, quality engineering, and AI‑enabled development, with an emphasis on enterprise applicability. 

• Promote a culture of knowledge sharing and practical application through workshops and training sessions, documentation, and cross‑team collaboration. 

• Contribute to documenting and showcasing R&D and AI‑enabled service capabilities for internal and business development use. 

Success Metrics: 

• Effective, governed adoption of AI within internal admin team and division’s R&D teams 

• Successful execution of external AI consulting engagements for divisions 

• Successful execution of internal AI initiatives for admin team 

• R&D Service Line delivery quality and consistency 

Required Skills & Experience: 

• Bachelor's degree in Computer Science or a related discipline; management education preferred. 

• 10-12+ years of experience in the software industry with a strong grounding in software development and/or software quality assurance. 

• 5+ years of experience in technical leadership and management roles. 

• Strong understanding and experience with SDLC, AI-SDLC maturity models, Agentic AI, coding practices, quality engineering, deployment and release considerations. 

• Strong hands-on experience of development tools and languages including .Net Framework, C#.Net, Java, Angular, React, TypeScript etc..  

• Strong hands-on experience of test automation tools including open source and commercial tools e.g. Playwright, Test Complete etc.  

• Ability to operate confidently at advisory, managerial, and consulting levels. 

• Excellent communication and stakeholder management skills.