SENIOR TECHNICAL LEAD

SENIOR TECHNICAL DEVELOPER

AI-led Java Modernisation | GKE on Google Cloud

Role Type

Hands-on Senior Individual Contributor

Experience

10-18 years

Location

India - Hybrid / Remote, subject to program needs

Preferred Domain

Airline, Travel or GDS

Target Platform

GKE on Google Cloud Platform

Employment Type

Full-time / Program Assignment

Role Purpose

We are seeking a highly capable, hands-on Senior Technical Developer to contribute to a large-scale technology modernisation programme. The role requires deep Java/J2EE engineering expertise, strong cloud-native development skills, and the ability to use both AI-assisted tools and AI-agentic engineering approaches to accelerate delivery without compromising quality, security, compliance, maintainability or operational resilience.

The successful candidate will work with minimal supervision, take end-to-end ownership of assigned outcomes, and contribute across analysis, design, build, testing, deployment and production-readiness activities. Prior experience in airline, travel or GDS platforms is strongly preferred.

Key Responsibilities

Hands-on Software Engineering

�Design, develop, test and maintain enterprise-grade applications using Java, J2EE, Spring Boot and related frameworks.

�Build secure, scalable and resilient microservices, REST APIs and event-driven components.

�Apply clean-code, SOLID, domain-driven design and twelve-factor application principles.

�Produce maintainable, testable and production-ready code with appropriate technical documentation.

�Perform code reviews, troubleshoot complex issues, optimise performance and reduce technical debt.

Modernisation Delivery

�Analyse and reverse engineer legacy applications, interfaces, data flows and business rules.

�Translate legacy functionality into modern service boundaries, APIs and cloud-native implementation patterns.

�Contribute to solution design, decomposition, dependency analysis, migration planning and cutover readiness.

�Create reusable engineering accelerators, templates and reference implementations for modernisation squads.

GCP and GKE Engineering

�Develop, containerise and deploy workloads on Google Kubernetes Engine within Google Cloud Platform.

�Work with Docker, Kubernetes manifests or Helm, configuration management, secrets, service accounts, ingress and workload identity.

�Contribute to CI/CD pipelines, automated quality gates, observability, logging, monitoring and production support.

�Engineer for reliability, scalability, fault tolerance, cost awareness and operational supportability.

Security, Risk and Compliance

�Apply secure-by-design and privacy-by-design practices throughout the software development lifecycle.

�Follow OWASP guidance, secure coding standards, least-privilege access, secrets management, encryption and API security practices.

�Remediate vulnerabilities identified through SAST, DAST, software composition analysis, container scanning and penetration testing.

�Maintain traceability and evidence required for enterprise governance, audit, change management and regulatory compliance.

�Ensure AI-generated outputs are reviewed, validated and handled in accordance with data classification, intellectual-property and responsible-AI policies.

Ownership and Collaboration

�Work independently with minimal supervision while proactively escalating risks, decisions and dependencies.

�Collaborate with architects, product owners, domain SMEs, platform teams, security teams, testers and SRE/operations teams.

�Mentor engineers, promote engineering standards and contribute constructively to design and code reviews.

�Demonstrate accountability for committed outcomes, quality, timelines and production readiness.

AI-related Skill Requirements

Candidates should demonstrate practical capability in both AI-assisted engineering and AI-agentic engineering. Tool familiarity alone is not sufficient; the candidate must be able to apply these capabilities responsibly within an enterprise delivery environment.

Skill Area

Expected Capabilities

Indicative Evidence / Tools

AI-assisted software engineering

Use generative-AI coding assistants for code completion, explanation, refactoring, test generation, documentation, defect analysis and developer productivity. Validate outputs against requirements, coding standards and architecture constraints.

GitHub Copilot, ChatGPT, Claude, Gemini, IDE-integrated assistants or equivalent.

Prompt and context engineering

Create precise prompts, task instructions, examples, constraints and acceptance criteria. Select and structure relevant artefacts so that AI outputs are grounded in approved program knowledge.

Prompt patterns, reusable instruction files, context packs, coding standards, architecture guidance and domain glossaries.

AI-agent configuration

Configure specialised agents with defined roles, goals, tools, workflow steps, guardrails, memory boundaries, escalation rules and output formats.

Agent definitions, system instructions, tool permissions, workflow orchestration, human-in-the-loop checkpoints.

Agent skills and reusable capabilities

Create or configure reusable agent skills for reverse engineering, design support, story elaboration, code generation, testing, review, documentation and troubleshooting.

Skill files, templates, procedures, tool adapters, validation rules and structured outputs.

Knowledge-context management

Curate and maintain trusted knowledge contexts drawn from requirements, legacy code, design documents, API specifications, coding standards, test assets and operational documentation.

Repository-based context, indexed documentation, retrieval workflows, metadata, versioning and access controls.

Agentic SDLC execution

Use coordinated agents across multiple SDLC steps while preserving traceability, deterministic quality gates and human accountability.

Multi-step workflows for analysis-to-code, code-to-test, review, defect remediation and documentation.

AI output evaluation and quality control

Assess accuracy, completeness, security, hallucination risk, code quality, test adequacy and alignment with architecture. Define feedback loops and measurable acceptance thresholds.

Golden datasets, benchmark tasks, review checklists, automated validations, regression testing and quality metrics.

Responsible and secure AI use

Protect confidential information, prevent unauthorised data exposure, respect licensing/IP constraints, and comply with enterprise AI governance and model-use policies.

Data classification, approved models, access controls, audit logs, prompt/output handling standards and responsible-AI controls.

AI-enabled delivery acceleration

Demonstrate measurable improvements in cycle time, engineering throughput, defect prevention or documentation quality without weakening controls.

Productivity metrics, reuse assets, reduced rework, improved coverage and evidence-based delivery outcomes.

Required Technical Skills

Java and Application Engineering: Java 11/17 or later; J2EE/Jakarta EE; Spring Boot; Spring Framework; Spring Data/JPA; Hibernate; REST/JSON/XML; Maven or Gradle; unit and integration testing.

Architecture and Integration: Microservices; API-first design; event-driven architecture; distributed systems; resilience patterns; domain-driven design; synchronous and asynchronous integration.

Cloud, Containers and DevOps: Google Cloud Platform; GKE; Docker; Kubernetes; Helm or equivalent; Git; CI/CD; infrastructure/configuration awareness; logging, monitoring and observability.

Data Engineering: Strong SQL; relational databases such as Oracle or PostgreSQL; schema design; query optimisation; transaction management; exposure to NoSQL or messaging platforms is advantageous.

Engineering Quality: Test automation; code coverage; static analysis; dependency management; performance engineering; peer review; production troubleshooting and root-cause analysis.

Security and Compliance: OWASP Top 10; secure coding; IAM; secrets management; encryption; API security; vulnerability remediation; DevSecOps controls; audit and change-management discipline.

Preferred Airline / Travel Domain Experience

�Passenger service, reservations, ticketing, pricing, check-in, departure control, loyalty, revenue management or airline operations.

�GDS, distribution, interline, codeshare, NDC or airline partner integrations.

�Modernisation of high-volume, mission-critical legacy airline platforms and interfaces.

�Understanding of availability, reliability, data integrity and operational continuity expectations within airline systems.

Behavioural Competencies

Competency

Expected Behaviour

Ownership

Takes end-to-end responsibility and follows through until outcomes are achieved.

Independence

Operates effectively with minimal supervision and seeks guidance only where decisions materially require it.

Problem solving

Breaks down ambiguity, diagnoses complex issues and makes evidence-based technical decisions.

Communication

Communicates clearly with technical and non-technical stakeholders; raises risks early.

Collaboration

Works constructively across architecture, product, domain, security, testing and platform teams.

Learning agility

Adopts new tools, AI practices and modern engineering patterns rapidly and responsibly.

Quality mindset

Balances delivery speed with security, maintainability, testing and operational resilience.

Qualifications

�Bachelor's or master's degree in Computer Science, Engineering, Information Technology or a related discipline, or equivalent practical experience.

�Relevant Java, GCP, Kubernetes or cloud-native certifications are desirable but not mandatory.

�Demonstrable hands-on engineering capability and delivery outcomes will be weighted more strongly than certification alone.

Measures of Success

Outcome Area

Success Measure

Delivery

Consistently completes committed outcomes with limited supervision and transparent risk management.

Engineering quality

Produces secure, maintainable, well-tested code with low defect leakage and manageable technical debt.

AI-enabled productivity

Uses AI-assisted and agentic practices to demonstrate measurable cycle-time, quality or throughput improvements.

Modernisation

Successfully translates legacy capabilities into scalable, supportable cloud-native services.

Security and compliance

Meets required security controls, evidence standards, governance gates and remediation timelines.

Cloud readiness

Deploys and supports reliable workloads on GKE/GCP with appropriate observability and operational controls.

Reuse and capability building

Creates reusable prompts, skills, agent configurations, engineering assets and patterns that improve squad performance.

Ideal Candidate Profile

The ideal candidate is a senior engineer who remains deeply hands-on, understands enterprise Java and cloud-native engineering, and treats AI as an engineering capability rather than merely a productivity tool. The candidate can configure AI agents, reusable skills and trusted knowledge contexts; evaluate AI-generated outputs rigorously; and apply these methods to accelerate modernisation delivery while maintaining security, compliance, quality and human accountability.

Assumptions Used in Preparing This Job Description

�The position is primarily a senior individual-contributor role rather than a people-management role.

�The target experience range is 10-18 years; this may be adjusted based on internal grade architecture.

�The modernisation programme uses Java/Spring-based microservices deployed to GKE on GCP.

�AI tools and models will be restricted to enterprise-approved platforms, repositories and data-handling policies.

�Airline domain experience is preferred, not mandatory, unless the role is assigned to a highly specialised functional area.

�The employer will insert company-specific information such as location, reporting line, equal-opportunity statement, benefits and application process before external publication.