Senior Technical Lead - Full Stack, Cloud & AI Engineering

1. Technical Architecture & Solution Design * Own and drive end-to-end technical architecture and solution design for applications and platforms. * Design scalable, secure, resilient and maintainable solutions across cloud, web, SaaS and enterprise application ecosystems. * Provide technical direction on application modernization, platform engineering, integrations and technology upgrades. * Evaluate technology choices, frameworks, design patterns and architectural trade-offs. * Ensure adherence to architecture standards, engineering best practices, security and performance requirements. * Work closely with enterprise architects, solution architects and other technology leaders on cross-platform initiatives.

2. Engineering Leadership & Hands-on Development * Lead and mentor engineering teams across frontend, backend, database and cloud technologies. * Provide technical guidance, conduct design and code reviews, and establish engineering standards. * Be hands-on when required, particularly in complex technical areas, critical implementations, debugging and proof of concepts. * Drive engineering quality through automated testing, code quality practices, CI/CD and DevOps principles. * Identify technical debt and drive initiatives to improve application maintainability, performance and reliability. * Encourage engineering ownership, technical curiosity and continuous learning within teams., * Provide technical leadership for complex production issues, recurring defects and critical application challenges. * Perform deep technical and functional analysis to identify root causes rather than relying on assumptions or superficial fixes. * Guide teams through incident resolution, problem management and permanent corrective actions. * Participate in complex support areas while ensuring operational stability and service quality. * Identify opportunities to improve system observability, application resilience and support processes. * Balance production support responsibilities with strategic engineering and transformation initiatives.

3. AI-Enabled Software Engineering * Actively leverage AI-assisted engineering tools and practices across the software development lifecycle. * Use AI tools for code generation, code reviews, debugging, test automation, documentation, technical research and solution design. * Understand AI-enabled SDLC practices and how they can improve engineering productivity and delivery quality. * Explore and implement practical opportunities to embed AI into development workflows and engineering processes. * Stay current with emerging AI technologies, coding assistants, agentic development and engineering automation. * Promote responsible and effective AI adoption within engineering teams.

4. Technical & Functional Analysis * Translate business requirements into technical solutions and actionable engineering plans. * Perform high-level architecture analysis as well as low-level technical investigation. * Understand application workflows, business processes, functional dependencies and system integrations. * Identify gaps, risks, dependencies and potential design challenges early in the delivery lifecycle. * Communicate technical complexities and trade-offs in a manner that is understandable to both technical and non-technical stakeholders.

5. Software Delivery & Project Governance * Understand and actively contribute to software delivery processes, Agile methodologies and project management practices. * Work closely with project managers, engineering managers, product owners and business stakeholders to drive delivery. * Provide technical estimates, identify delivery risks, manage dependencies and support release planning. * Ensure appropriate engineering governance, documentation, traceability and delivery discipline. * Be comfortable working with tools such as Jira, ServiceNow, Git, Slack, Microsoft Teams and CI/CD platforms.

6. Stakeholder Management & Communication * Effectively communicate with developers, architects, engineering managers, business teams and executive leadership. * Present technical solutions, architecture decisions, risks and recommendations with clarity. * Drive technical discussions, design reviews and cross-team collaboration. * Convert complex technical challenges into clear business implications and actionable recommendations. * Demonstrate confidence in engaging with senior stakeholders and influencing technology decisions.

Required Technical Skills & Experience

Cloud: AWS, Azure or equivalent

Backend: Java, Spring Boot, Microservices

Frontend: Angular or React

Web: Modern web architectures, APIs, integrations

Databases: Oracle, SQL and relational databases

Data Platforms: NoSQL, Data Lakes, modern data platforms

DevOps: Git, CI/CD, containers, automation

AI Engineering: AI coding assistants, AI-enabled SDLC, agentic engineering

Delivery Tools: Jira, ServiceNow, Slack, Microsoft Teams, * Strong technical ownership and architectural direction across assigned applications and platforms.

• Improved engineering quality, application stability and overall technical health.
• Faster and more effective resolution of complex technical issues.
• Successful delivery of strategic engineering and modernization initiatives.
• Measurable improvements in engineering productivity through AI-assisted development.
• Better collaboration between engineering, architecture, operations and business teams.
• Development of a technically capable, accountable and self-sufficient engineering team.

The ideal candidate is a technology leader who can think like an architect, execute like a senior engineer, troubleshoot like an expert and communicate like a business partner.

The ideal candidate should be a hands-on technology leader with the ability to architect scalable solutions, lead complex engineering initiatives, guide development teams and contribute directly to coding and troubleshooting when required.

The role requires someone who can navigate across application development, cloud infrastructure, databases, SaaS platforms and enterprise integrations, while balancing new development initiatives with complex production support responsibilities.

We are particularly interested in an AI-enabled engineering leader who actively leverages AI tools and practices to improve software engineering productivity, accelerate delivery, enhance code quality and drive innovation.

The candidate should be comfortable working with ambiguity, independently analyzing complex technical and functional problems, communicating effectively with diverse stakeholders and translating business requirements into practical technology solutions., Cloud & Infrastructure

• Strong experience with AWS or equivalent cloud platforms.
• Understanding of cloud-native architecture, serverless computing, containers, scalability, security and infrastructure automation.
• Exposure to cloud migration, modernization and SaaS deployment models.

Application & Web Technologies

• Strong experience in Java-based enterprise application development.
• Experience with Angular or React and modern web application architectures.
• Understanding of REST APIs, microservices, enterprise integrations and distributed systems.
• Exposure to SaaS products, multi-tenant architectures and platform-based application development.

Databases & Data Platforms

• Strong experience with Oracle or equivalent enterprise relational databases.
• Exposure to NoSQL databases, data lakes and modern data platforms.
• Ability to analyze database architecture, data models, query performance and integration patterns.
• Understanding of data security, scalability and data management principles., * Experience in Media & Entertainment is highly valuable.
• Understanding of media workflows, content lifecycle management, digital platforms, content distribution, metadata, rights management or media asset management would be an advantage.
• Experience working with global engineering teams, enterprise platforms and Global Capability Centers (GCCs).
• Exposure to SaaS products (ex. Salesforce CRM, SFDC) and large-scale enterprise technology ecosystems.

Desired Leadership Attributes

Strong Technical Depth: Commands respect through technical knowledge, architectural thinking and the ability to solve complex engineering problems.

Architect Mindset with Hands-on Ability: Can operate at an enterprise architecture level but is equally comfortable diving into code, debugging and implementation when required.

AI-First Engineering Mindset: Actively explores and adopts AI tools to improve the way software is designed, developed, tested and delivered.

Comfortable with Ambiguity: Can work with incomplete requirements, navigate uncertainty and independently figure out solutions without waiting for detailed instructions.

Strong Analytical Thinking: Capable of connecting technical and functional dots, performing deep-dive analysis and identifying underlying problems.

Clear Communication: Communicates effectively across all levels, from developers to architects, business leaders and executives.

Ownership & Accountability: Takes responsibility for technical outcomes, drives decisions and ensures issues and initiatives are taken to closure.

Continuous Improvement: Constantly looks for opportunities to modernize technology, improve engineering practices, reduce technical debt and increase delivery effectiveness.