1 day ago
Madrid, SpainSenior
Responsibilities
- Design and develop cloud-based AI applications and services integrating LLMs and generative AI capabilities.
- Build backend REST/GraphQL services and microservices that integrate LLMs with internal and external APIs.
- Develop RAG pipelines covering ingestion, chunking, embeddings, vector storage, retrieval, and agentic workflows.
- Implement CI/CD, containerization, observability, testing, quality gates, and API security for production systems.
- Apply LLMOps/MLOps practices including prompt and artifact versioning, evaluation, monitoring, dashboards, and guardrails.
- Collaborate with architecture, data, security, and business teams on scalability, resilience, cost, governance, and ethical AI.
- Document architectural decisions, create reusable playbooks and accelerators, review code, and mentor junior engineers.
Requirements
- 3–7+ years of software engineering experience depending on seniority, including API design, integration patterns, concurrency/asynchronous programming, and software engineering best practices.
- Experience with Python and backend frameworks such as FastAPI, Flask, or Express.
- Advanced Git knowledge and experience with GitHub Actions, GitLab CI, Azure DevOps, or Jenkins.
- Experience with at least one of Azure, AWS, or GCP and containerized deployment using Docker; Kubernetes is desirable.
- Experience integrating generative AI and LLM services such as Azure OpenAI, OpenAI, Vertex AI, or Amazon Bedrock.
- Practical knowledge of RAG, embeddings, vector stores, and orchestration frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
- Experience with testing, monitoring, API security, LLMOps/MLOps, experiment tracking, and evaluation dashboards.
- Professional English proficiency for documentation and international collaboration.
- Preferred experience with Terraform, advanced Kubernetes, Pub/Sub, Kafka, observability and reliability practices, GenAI security and compliance, and Agentic AI.
- Ability to collaborate across disciplines, communicate clearly, document decisions, and mentor junior profiles.
Benefits
- Hybrid work model and flexibility supporting work-life balance.
- Training, online and in-person learning, cloud and data governance education, certifications, mentoring, and professional growth opportunities.
- Varied multisector projects involving generative AI, Business AI, cloud, data, and emerging technologies.
- Collaborative, multicultural, inclusive teams and opportunities to work with leading organizations.
- Opportunities for technical challenge, autonomy, reusable accelerators, and role expansion within the Data & AI area.
Tech Stack
Apache KafkaAWSAzureDockerExpressFastAPIFlaskGitGitHub ActionsGitLab CI/CDGoogle Cloud PlatformGraphQLJenkinsKubernetesMLflowPythonTerraform
Categories
About Capgemini
Capgemini is a global IT services and consulting firm that delivers strategy, cloud, AI, software engineering, and managed services to large enterprises and public-sector clients. Founded in 1967 and headquartered in Paris, it is publicly traded on Euronext Paris and operates in 50+ countries. The group expanded its engineering capabilities by acquiring Altran in 2020, now operating as Capgemini Engineering.