5 months ago
Madrid, SpainMid Level / Senior
H1B Sponsor
Responsibilities
- Design and develop cloud-based AI applications and services integrating LLMs and generative AI capabilities.
- Build backend REST and GraphQL services, microservices, and integrations with internal and external APIs.
- Develop RAG pipelines covering ingestion, chunking, embeddings, vector storage, and retrieval.
- Build agents with LangChain, LlamaIndex, Semantic Kernel, or comparable orchestrators, including tool and function calling.
- Implement CI/CD, containerization, quality gates, testing, observability, and API security controls.
- Apply LLMOps/MLOps practices including prompt and artifact versioning, evaluation, monitoring, cost tracking, drift detection, and guardrails.
- Collaborate with architecture, data, security, and business teams on scalability, resilience, cost, governance, compliance, and AI ethics.
- Document architecture 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 or asynchronous programming, and strong coding practices.
- Professional experience with Python and backend frameworks such as FastAPI, Flask, or Express.
- Advanced Git experience and experience with CI/CD using GitHub Actions, GitLab CI, Azure DevOps, or Jenkins.
- Experience with at least one major cloud platform—Azure, AWS, or GCP—and containerized workloads using Docker; Kubernetes is desirable.
- Practical experience integrating generative AI and LLM services, including API consumption, authentication, cost and limit management, prompting, and basic evaluation.
- Practical knowledge of RAG, embeddings, vector stores, and orchestration frameworks such as LangChain, LlamaIndex, or Semantic Kernel.
- Experience with unit, integration, or contract testing; monitoring such as OpenTelemetry; and basic API security including OAuth2/OIDC, secrets, and rate limiting.
- Experience with LLMOps/MLOps tools and practices, including MLflow, Weights & Biases, Prompt Flow, Azure AI Studio evaluations, experiment tracking, and evaluation dashboards.
- Professional English proficiency and clear communication, documentation, collaboration, and continuous-learning skills.
- Preferred experience includes Terraform, advanced Kubernetes, event-driven systems using Pub/Sub or Kafka, SLO/SLA practices, alerting, chaos testing, GenAI security and compliance, and agentic AI.
Benefits
- Hybrid work model with flexibility and a culture focused on work-life balance.
- Training, online and in-person learning, and professional certifications.
- Exposure to varied, multisector projects involving generative AI, Business AI, cloud, data governance, and emerging technologies.
- Collaborative, inclusive, multicultural teams and opportunities for mentoring and professional growth.
- Technical challenge, autonomy, and opportunities to expand responsibilities within the Data & AI area.
Tech Stack
Apache KafkaAWSAzureDockerExpressFastAPIFlaskGitGitHub ActionsGitLab CI/CDGoogle Cloud PlatformGraphQLJenkinsKubernetesMLflowPythonTerraform
Categories
About Capgemini
Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2025 global revenues of €22.5 billion. Make it real | www.capgemini.com