Full Stack Engineer

Role Overview We are seeking a Full Stack Lead Engineer who brings a strong foundation in software engineering combined with AI-enabled development practices . This role is ideal for a technology leader who has evolved across backend, frontend, cloud, and DevOps ecosystems , and is comfortable operating in complex, multi-technology environments involving both modern and legacy systems. You will play a dual role — acting as a hands-on technical leader and a client-facing solution partner , driving end-to-end engineering excellence across product development initiatives. Key Responsibilities 1. Technical Leadership & Delivery Lead end-to-end design and development of scalable, high-quality software solutions Work across full-stack layers : Backend, Frontend, APIs, integrations, and data systems Own and drive software release cycles including planning, development, testing, and deployment Ensure adherence to engineering best practices, coding standards, and quality benchmarks Conduct thorough code reviews (PR reviews) and enforce clean, maintainable architecture 2. AI-Augmented Engineering Leverage AI tools such as GitHub Copilot, Claude Code, Cursor, Codex , and similar platforms for: Code generation and acceleration Debugging and root cause analysis Automated code reviews and quality checks Drive adoption of AI-assisted development practices across teams Ensure responsible and efficient use of AI tools in engineering workflows 3. Solutioning & Architecture Collaborate with clients and internal stakeholders to understand business requirements Lead technical solutioning, system design, and architecture decisions Work across diverse technology stacks (e.g., .NET, Java, Node.js, Python, MERN ) Design systems that integrate across cloud platforms, third-party services, and legacy systems 4. Team Leadership & Governance Lead and mentor a team of engineers across different technology stacks Provide technical guidance, resolve blockers, and ensure delivery excellence Establish and enforce development governance frameworks Drive continuous improvement in engineering practices, productivity, and quality 5. DevOps & Cloud Engineering Oversee CI/CD pipelines, release management, and deployment strategies Ensure system scalability, performance, and reliability on cloud platforms (AWS, Azure, or GCP) Work closely with DevOps teams to implement automation, monitoring, and observability 6. Client Engagement & Communication Act as a technical point of contact for clients Drive technical discussions, provide recommendations, and build client trust Communicate complex technical concepts clearly to both technical and non-technical stakeholders Contribute to pre-sales, estimations, and proposal solutioning Required Skills & Experience Core Engineering Expertise Strong experience in at least one primary stack: .NET / Java / Node.js / Python / MERN Proven experience across: Backend development (APIs, microservices) Frontend frameworks (React, Angular, Vue, Next.js , Typescript, Javascript etc.) Database systems (SQL/NoSQL) Full Stack & Platform Exposure Hands-on experience with cloud platforms (AWS, Azure, or GCP) Exposure to DevOps practices (CI/CD, containerization, infrastructure as code) Experience working in multi-stack, distributed system environments AI-Enabled Development Practical experience using AI tools like: GitHub Copilot, Claude Code, Cursor, Codex Strong understanding of AI-assisted coding, debugging, and review workflows Ability to integrate AI into engineering productivity and quality processes Leadership & Client Skills Proven experience in leading engineering teams Strong communication and stakeholder management skills Experience in client-facing roles , including solutioning and technical discussions Ability to balance hands-on coding with leadership responsibilities What We Are Looking For Engineers who have grown beyond a single technology stack Individuals who embrace continuous learning and cross-functional expertise Leaders who can drive engineering excellence while staying hands-on Professionals who are comfortable working in fast-paced, evolving technology landscapes Success Metrics Delivery of high-quality, scalable solutions Effective adoption of AI-augmented engineering practices Strong client satisfaction and engagement outcomes Improved team productivity and engineering quality benchmarks