Principal Software Engineer

Overview

Hands-on principal engineering leader who defines architecture, solves complex engineering problems, raises technical standards across teams, and establishes AI as a foundational engineering capability across the SDLC.

What you'll do

• Champion AI-first engineering practices across requirements analysis, architecture, design, coding, code review, testing, documentation, deployment, operations, incident response, and support.
• Identify and implement opportunities to automate repetitive engineering, testing, documentation, troubleshooting, and operational work using approved AI capabilities.
• Lead technical adoption of AI-assisted development platforms, engineering copilots, autonomous agents, and AI-enabled developer workflows.
• Create reusable prompts, agent patterns, implementation blueprints, reference solutions, and playbooks that enable effective and consistent AI adoption across teams.
• Use measurable engineering outcomes to evaluate AI adoption, including cycle time, throughput, deployment frequency, defect trends, automation coverage, maintainability, and developer effectiveness.
• Develop software designs, system architecture diagrams, engineering specifications, decision records, and implementation roadmaps aligned with business, customer, product, and stakeholder objectives.
• Design and implement new components and subsystems, and review or modernize existing architectures when required.
• Provide hands-on leadership through architecture reviews, design reviews, code reviews, technology evaluations, prototypes, and critical implementation work.
• Guide teams in the appropriate use of Generative AI services, LLMs, RAG, agentic workflows, Model Context Protocol (MCP) servers, and emerging AI technologies.
• Ensure solutions meet enterprise expectations for scalability, performance, availability, fault tolerance, maintainability, observability, extensibility, data privacy, and security.
• Define consistent product and system patterns that improve quality, integration, reuse, and cross-team collaboration.
• Establish practical engineering standards and validation controls for AI-generated code, tests, designs, documentation, and operational artifacts.
• Ensure AI-assisted solutions comply with enterprise governance, security, privacy, risk management, licensing, intellectual property, and regulatory requirements.
• Require human accountability for critical technical decisions and validate AI-generated output through code review, automated testing, security scanning, performance testing, and production readiness checks.
• Balance rapid experimentation and innovation with engineering discipline, architectural integrity, operational reliability, and responsible AI practices.
• Drive technical execution of complex, high-impact initiatives across multiple teams and geographies, managing technical risks, dependencies, and integration concerns.
• Improve SDLC effectiveness through automation, CI/CD, test engineering, observability, secure development practices, and AI-enabled workflows.
• Own non-functional requirements and ensure software is production-ready, well-instrumented, compliant, secure, and designed for always-on, high-transaction distributed environments.
• Diagnose complex system failures and production issues; lead root cause analysis, remediation, reliability improvements, and technical debt reduction.
• Partner with Product, Architecture, Security, SRE, Platform, Quality Engineering, and business stakeholders to translate functional and non-functional needs into implementable technical solutions.
• Mentor senior engineers, technical leads, and developers in architecture, design principles, design patterns, coding practices, testing, production engineering, and AI-assisted development.
• Raise engineering capability through technical forums, design walkthroughs, workshops, communities of practice, and documented engineering standards.
• Influence technology choices and engineering direction across teams without relying on direct authority.
• Build AI proficiency through coaching on prompt engineering, context engineering, AI-assisted design, AI-powered testing, AI-driven troubleshooting, and effective use of autonomous agents.
• Communicate complex technical concepts, options, risks, and recommendations clearly to technical and non-technical audiences.
• Evaluate emerging technologies and lead Proofs of Concept (PoCs), pilots, and adoption plans based on measurable engineering and business value.
• Continuously improve developer experience, engineering tooling, architecture practices, and delivery workflows.
• Contribute to enterprise engineering strategy and promote learning, experimentation, knowledge sharing, and evidence-based technical decision-making.

What you'll need

• Bachelor's or Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related discipline.
• 10+ years of professional software engineering experience delivering enterprise-scale solutions, including significant hands-on development and architecture responsibility.
• Proven experience providing technical leadership across distributed Agile engineering teams and complex cross-functional initiatives.
• Demonstrated success designing and delivering cloud-native, high-volume, fault-tolerant, and globally distributed systems.
• Strong hands-on programming experience in Java and related enterprise technologies, including Spring, Spring Boot, J2EE, object-oriented analysis and design, and modern engineering practices.
• Deep knowledge of microservices, APIs, event-driven architecture, distributed systems, enterprise integration, REST, messaging, relational and NoSQL databases, and application containerization.
• Practical cloud engineering experience.
• Strong knowledge of CI/CD, automated testing, Test-Driven Development, DevSecOps, observability, system monitoring, JVM diagnostics and tuning, networking, and production troubleshooting.
• Experience with SaaS, PaaS, IaaS, cloud migration strategies, scripting using Python or Bash, and modern build and deployment tooling.
• Strong analytical, system-thinking, problem-solving, communication, and stakeholder-influence skills.
• Hands-on experience with AI-assisted software development platforms such as GitHub Copilot, Microsoft Copilot, Cursor, Claude Code, or equivalent technologies.
• Strong understanding of Generative AI, LLMs, RAG architectures, agentic workflows, MCP servers, prompt engineering, context engineering, and AI governance.
• Experience evaluating, integrating, and scaling AI capabilities within enterprise engineering environments.
• Ability to define and apply standards for secure, responsible, and high-quality AI-assisted software development.
• Demonstrated ability to connect AI adoption to measurable improvements in engineering productivity, software quality, or developer experience.

Nice to have

• Google Cloud Platform experience.
• Experience in the travel technology, payments, or other high-transaction regulated domains.
• Experience building AI-enabled developer platforms, internal engineering tools, intelligent automation, or production-grade AI applications.
• Experience leading architecture modernization, cloud migration, platform engineering, or operational excellence initiatives.
• Demonstrated contribution to technical communities, architecture forums, engineering standards, patents, publications, or open-source initiatives.

Details

• Location: Bengaluru, Karnataka, India.
• Work mode: Hybrid.