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Salary
≈ $15k – $35k per year (Estimated)
Location
In office (Hyderabad)
Seniority
Senior · 4+ years exp

First seen by Alion on Oct 28, 2026. L'Oréal scores A on the Alion truth index.

Overview
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Profile match
L'Oréal is a French cosmetics and beauty group headquartered in Clichy, near Paris, that develops and sells hair care, skin care, makeup and fragrance products. Founded in 1909, it is the largest cosmetics company in the world and runs divisions such as L'Oréal Luxe, Consumer Products and Professional Products, with brands including Kérastase, Redken, Maybelline and licensed YSL and Prada beauty lines. Its openings cover sales account executives, marketing and brand directors, education trainers, supply chain, quality engineering, scientific and regulatory roles, finance and internships worldwide.

We are looking for a hands-on Senior Software Engineer to join our development squad and work directly with the Technical Lead to execute our integration strategy. You will be responsible for the "build" part of the blueprint. This role focuses on delivering robust, high-performance REST/gRPC APIs and event-driven microservices using Python, FastAPI, and cloud-native messaging on Google Cloud Platform (GCP). While Python is our primary stack, proven experience with Java and Spring Boot is a significant plus for polyglot integration projects. You will collaborate with the Tech Lead to ensure AI coding assistants are used effectively, code adheres to security benchmarks, and integrations are deployed reliably on GCP.


The candidate will have responsibilities across the following functions:


AI-Augmented Development (Execution Focus):

  • AI Productivity: Actively use GitHub Copilot, Cursor, or equivalent to generate code, complex data mappings, and integration test suites, following the squad's standardised "Prompt Library" established by the Tech Lead.
  • Code Quality and Security: Review AI-generated code to prevent "hallucinated" vulnerabilities, ensuring all output meets performance and security benchmarks before submission.
  • Test Automation: Leverage AI to generate comprehensive unit and integration test suites for both Python and (where applicable) Java components, increasing test coverage and reducing manual testing overhead.


API and Integration Build:

  • Development: Implement core business logic using FastAPI, Cloud Pub/Sub, or Cloud Tasks to support integration patterns (e. g., Saga, Event-Driven) defined by the Solution Architect and Tech Lead.
  • API Craftsmanship (Python): Design, build, and refine RESTful and gRPC APIs with a focus on implementation details including custom middleware, dependency injection, error handling, and response serialisation using FastAPI or Flask.
  • Polyglot Support (Java): Contribute to or maintain API services written in Java/Spring Boot where required, supporting cross-team integration needs or legacy system migrations.
  • Cloud-Native: Develop microservices optimised for GCP (GKE, Cloud Run, Pub/Sub, Cloud Functions) or Azure (AKS, App Service, Service Bus), following the squad's deployment standards, with GCP as the primary target.


Python, GCP and Java Excellence (The "How"):

  • Python Ecosystem (Mandatory): Deep-dive into FastAPI async capabilities, Pydantic data validation, SQLAlchemy/ORM configurations (optimisation, caching strategies, N+1 query prevention), and Flask/Django REST framework where applicable.
  • GCP Ecosystem (Mandatory): Hands-on expertise with GKE, Cloud Run, Pub/Sub, Cloud Functions, Cloud SQL/Spanner, IAM, and Cloud Build for CI/CD, following GCP Well-Architected best practices.
  • Java Ecosystem (Preferred): When working in Java, adhere to best practices using Spring Boot 3. x and Spring Data/Hibernate for legacy maintenance or integration layers.
  • Performance Tuning: Profile API endpoints and database access layers across both stacks to ensure low latency and high throughput.


Collaboration with Tech Lead:

  • PR Participation: Participate in Pull Request reviews, upholding the quality gates set by the Tech Lead. Provide constructive feedback to peers to maintain a clean, scalable codebase.
  • Mentorship: Mentor junior developers in Python best practices, GCP deployment patterns, and effective AI tool usage. Act as a resource for Java-related questions where applicable.
  • CI/CD Execution: Work alongside the Tech Lead and DevOps to refine CI/CD pipelines (Git, Cloud Build, GitHub Actions, or Jenkins) targeting GCP, ensuring smooth, zero-downtime deployments.


Requirements:

  • AI Tooling: Proven experience utilising AI coding assistants (GitHub Copilot, Cursor, or equivalent) to accelerate development and troubleshooting in both Python and Java ecosystems.
  • Python (Core): Expert-level proficiency in Python 3.11+, with solid experience in FastAPI, Pydantic, SQLAlchemy, and asynchronous programming (asyncio).
  • Google Cloud Platform (Core): 3+ years of hands-on experience deploying and operating microservices on GCP (GKE, Cloud Run, Pub/Sub, Cloud Functions, Cloud SQL/Spanner, IAM). Google Professional Cloud Developer certification is a strong plus.
  • Messaging and Integration: Hands-on experience with event-driven architectures using Cloud Pub/Sub, Kafka, or RabbitMQ.
  • API Protocols: Strong experience in building REST and gRPC APIs with robust error-handling and observability (Cloud Logging, Cloud Monitoring, OpenTelemetry).
  • Java and Spring (Plus): Proven experience with Java 17/21 and Spring Boot 3. x in a production environment is a significant plus for polyglot integration projects.
  • Infrastructure as Code: Familiarity with Terraform or Deployment Manager for provisioning GCP resources is a plus.


Soft Skills:

  • Experience: 5+ years in software engineering, with the majority in Python and GCP, and at least 1-2 years working with Java/Spring.
  • Mentorship: Experience mentoring junior developers, providing thorough code reviews, and promoting engineering best practices across multiple languages.
  • Pragmatism: Ability to balance writing perfect code with delivering business value, especially when leveraging AI tools for speed.
  • Collaboration: Excellent communication skills to clearly articulate technical trade-offs to the Tech Lead, Architects, and cross-functional stakeholders.
  • Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent experience).

Skills

FastAPI, GCP, Google Cloud Platform, Kafka, Microservices, Pub/SUB, Python, gRPC

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