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
