Lead Full Stack Developer — Systems Modernization & AI Integration

Location: HSR Layout, Bengaluru, India Job Overview We’re looking for a Lead Full Stack Developer who can own a product end to end, from React/Angular interfaces down to Node.js services, data models, and cloud infrastructure. You’ll lead the move from monolith to microservices, integrate AI capabilities into existing products, turn business needs into technical requirements, and set engineering standards as we scale. Key Responsibilities Full stack delivery • Build responsive React and/or Angular frontends with attention to reusability, state management, and performance. • Build scalable, reusable Node.js services and REST/event-driven APIs serving SPAs and AI- augmented workflows. • Own the full request path (UI, API contract, service layer, database) and make deliberate trade-offs across the stack. • Implement and maintain MongoDB and relational databases with good schema design, indexing, performance, and security. Modernization • Lead the decomposition of monoliths into independently deployable microservices and modular frontends. • Assess legacy codebases and technical debt, and define migration paths that don’t disrupt production. • Replace legacy UI screens incrementally alongside backend services, managing old and new in coexistence. AI integration • Identify integration points between legacy systems and AI/ML capabilities, and design clean interfaces between them. • Expose legacy logic and data to AI agents through well-designed APIs, and build the frontend experiences on top. Technical stakeholder management • Act as the primary technical interface for engineering teams, product owners, data teams, and external partners. • Run requirement-gathering sessions and convert ambiguous asks into clear technical requirements, API contracts, and estimates. • Drive execution: sequence work, flag dependencies and risks early, and keep dependent teams updated. • Explain technical trade-offs and timelines to non-technical audiences in language they can act on. Engineering leadership • Review code across frontend and backend, and mentor engineers on standards and architecture. • Document architecture, schemas, API contracts, and migration decisions clearly. • Collaborate with developers, data scientists, and product owners to ship quality software. • Use AI tools (coding assistants, code review, test generation, documentation) to lift team productivity, and set the guardrails for where their output must be reviewed and owned by an engineer. Required Skills & Experience • 5+ years of software development experience with real depth on both frontend and backend, ideally in product companies or fast-scaling startups. • Strong proficiency in JavaScript/TypeScript, Node.js, and Express.js. • Strong hands-on React and/or Angular: component architecture, state management (Redux, Context, RxJS, NgRx), routing, and build tooling. • Solid frontend fundamentals: HTML5, CSS3, responsive design, and browser performance profiling. • Hands-on MongoDB experience: schema design, indexing, and query optimization. • Hands-on experience with a relational database, MySQL preferred (PostgreSQL or similar also fine): data modelling, joins, indexing, and query tuning. • Judgement on when to use a relational store versus a document store, and comfort running both in the same system. • Demonstrated experience breaking monolithic systems into microservices. • Ability to read, refactor, and wrap legacy codebases rather than always starting fresh. • RESTful API design with a solid grasp of HTTP, async patterns, and event-driven design. • Familiarity with JWT and OAuth 2.0 across client and server. • Strong Git practices, including branching strategies for large refactors. • Experience with Docker and CI/CD pipelines. • Comfort with Agile (Scrum, Jira) and with leading sprint planning and backlog prioritization. • Excellent written and verbal communication; this role involves constant stakeholder interaction. AI & Modernization Experience (Key Differentiator) • Integrating AI/ML into backend systems: LLM APIs (OpenAI, Anthropic, etc.), vector databases, embeddings, or RAG pipelines. • Building AI-facing experiences: streaming responses, chat and copilot interfaces, human-in-the-loop flows. • AI integration patterns: prompt orchestration, tool/function calling, semantic search, and async AI workflows. • Data pipelines for AI workloads: extraction from legacy systems, transformation, and delivery. • Pragmatic incremental modernization: strangler fig patterns and backward compatibility during transitions. Additional Required/Preferred Skills • Working knowledge of AWS: EC2, ECS, S3, IAM, VPC, CloudWatch, ALB, Route 53, CloudFront. • CI/CD with GitHub Actions, GitLab CI, or Jenkins, including frontend build and deployment pipelines. • Automation scripting in Node.js, Bash, or Python. • Deploying containerized applications with Docker and Amazon ECS/Fargate. • Solid Linux fundamentals and networking concepts: DNS, TCP/IP, HTTPS, SSL/TLS, load balancers, reverse proxies. • Infrastructure as Code (Terraform or CloudFormation). • Observability and monitoring, including CloudWatch and frontend error/performance monitoring. • Secure coding practices, secrets management, and cloud security. Nice-to-Have • Frontend testing (Jest, React Testing Library, Cypress, Playwright). • Design systems, component libraries, or micro-frontend architectures. • Server-side rendering (Next.js, Nuxt, Angular Universal). • Message queues and event streaming (Kafka, RabbitMQ, AWS SQS/SNS). • API gateway or service mesh patterns (Kong, AWS API Gateway), GraphQL, or gRPC. • Feature flags, canary, or blue-green deployment strategies. • Open-source or technical community contributions. Personal Attributes • Systems thinker who sees both the modernization big picture and the day-to-day tactical steps. • Strong communicator who holds their own in cross-team discussions and with non-technical stakeholders. • Comfortable being the single technical point of contact: gathering requirements, setting expectations, closing the loop. • Ownership and attention to detail, especially when changes affect production. • Uses AI to move faster day to day, but keeps design, correctness, and production calls firmly human. • Radical Execution: You thrive in ambiguity. If the road is blocked, you find a side path. • Courageous Advocacy: You’ll say “AI isn’t the right solution for this” when the data says so.