Senior AI Engineer | Remote

RESPONSIBILITIES

• Lead the construction and evolution of AI systems in production, combining batch and streaming data pipelines, backend services, and deployment of machine learning and LLM models.
• Design and implement robust pipelines for ingestion, transformation, and serving of structured, semi-structured, and unstructured data, with a focus on generative AI applications, RAG, and advanced analytics.
• Develop scalable backend services for real-time and batch inference, using REST/gRPC/GraphQL APIs, ensuring versioning, observability, and SLAs.
• Design event-driven distributed architectures for intelligent applications, using tools like Kafka, Pub/Sub, Kinesis, or SQS/SNS.
• Define standards and best practices for relational, non-relational, and vector databases, including embedding modeling, feature stores, and retrieval strategies for RAG.
• Operate AI workloads in the cloud and Kubernetes, including GPU optimization, autoscaling, scheduling, and cost control.
• Implement MLOps and LLOps pipelines, including CI/CD for models, drift monitoring, dataset versioning, and experiment tracking.
• Ensure governance, quality, traceability, and security of data and models throughout their lifecycle.
• Act in the design and optimization of data and AI architecture, prioritizing performance, reliability, cost-effectiveness, and security.
• Anticipate technical risks in pipelines, inference, and infrastructure, proposing scalable and resilient solutions.
• Support the construction of internal data and AI platforms (data platform / AI platform), promoting standardization and reuse of components.
• Collaborate with multifunctional product, engineering, and data science squads, leading strategic technical decisions.
• Mentor mid-level and junior engineers, disseminating best practices in software, data, and AI engineering.
• Actively contribute to the corporate AI strategy, aligning technology with business objectives and product evolution.

MANDATORY REQUIREMENTS

• Minimum of 5 years of experience in Data Engineering, Backend, ML Engineering, or AI Engineering.
• Solid experience in developing batch and streaming pipelines in production.
• Proven experience in deploying and operating ML or LLM models in production.
• Experience in distributed and event-driven architecture.
• Mastery of Python, SQL, and API development.
• Experience with containers and Kubernetes.
• Experience in cloud (AWS, GCP, or Azure) and data governance best practices.
• Experience in system observability (structured logs, metrics, tracing).
• Experience in optimizing pipeline and service performance.
• Strong technical communication skills with technical and non-technical stakeholders.

DESIRED REQUIREMENTS

• Experience with LLM and RAG frameworks (LangChain, LlamaIndex, etc.).
• Experience with Ray, Spark, Flint, Beam, or distributed processing systems.
• Experience with experiment tracking and MLOps platforms (ClearML, MLflow, Vertex AI, SageMaker).
• Advanced knowledge in infrastructure as code (Terraform, Helm, CDK).
• Experience deploying scalable applications on Kubernetes.
• Experience with vector databases and semantic search systems.
• Experience with GPU workloads and cost optimization in the cloud.
• Track record of technical leadership in complex projects or internal platforms.

ESSENTIAL SOFT SKILLS

• Clear and effective communication at all levels of the organization.
• Ability to lead complex technical discussions and architectural decisions.
• Mentorship and technical development skills for the team.
• Systemic vision to assess risks, trade-offs, and opportunities.
• Autonomy and initiative in solving complex problems.
• Platform ownership mindset, focused on reliability, cost, and developer experience.

VALUED PROJECTS / ACHIEVEMENTS

• Implementation of AI systems in production with high business impact.
• Construction of streaming pipelines or internal data/AI platforms.
• Deployment of RAG systems with corporate data.
• Proven optimization of infrastructure costs or GPU usage.
• Creation of scalable and resilient distributed architectures.
• Definition of organizational standards for data or AI engineering.