Senior Agentic AI Engineer

• Competitive salary package: We adapt to your experience and salary expectations.
• Flexible compensation: Health insurance, meal vouchers, training benefits, and more.
• Free professional training to support your career development.
• Free English classes, depending on your level.
• The opportunity to participate in challenging international projects involving innovative technologies.
• A collaborative, dynamic, and supportive working environment.
• Opportunities for professional growth in a continuously evolving sector.

Candidates holding a disability certificate of 33% or higher will be positively considered.

At MBC, equity, diversity, and inclusion are fundamental to our recruitment policy. We are committed to fostering an inclusive, respectful workplace where everyone has the opportunity to develop their talent.

Join a growing international consulting company and take the next step in your professional career. Work on innovative AI projects, collaborate with talented professionals, and contribute to the development of next-generation intelligent systems.

At MBC, we are continuing to grow, and we are now looking for a Senior Agentic AI Engineer to join an innovative project focused on Agentic AI, multi-agent systems, advanced machine learning, and intelligent engineering solutions.

We are looking for a highly skilled professional with strong expertise in AI architecture, LLMs, Python, C++, and production-grade AI systems, who is passionate about building scalable, reliable, and cutting-edge AI solutions.

We are looking for a Senior Agentic AI Engineer with extensive experience designing, developing, and deploying production-ready AI agents and multi-agent architectures.

You will play a key role in building intelligent systems that combine LLMs, advanced reasoning, model orchestration, retrieval-augmented generation (RAG), and engineering intelligence.

The ideal candidate has a strong software engineering background, hands-on experience with LangGraph, agent communication protocols, model training, and MLOps, and the ability to transform complex technical requirements into robust AI solutions.

• Design, develop, and deploy multi-agent orchestration architectures using LangGraph, including planning, routing, reasoning loops, state management, and human-in-the-loop approvals.
• Build Mixture-of-Agents (MoA) workflows to coordinate specialized agents and improve decision-making.
• Implement Mixture-of-Experts (MoE) and Mixture-of-Thoughts (MoT) strategies to optimize model performance, latency, and cost.
• Develop secure agent interoperability using MCP, A2A, and ACP protocols.
• Implement agent memory, context management, tool calling, approval workflows, and traceability.

LLMs, RAG & Model Development

• Build advanced RAG pipelines, including document extraction, embeddings, vectorization, and hybrid search.
• Work with vector databases such as pgvector, Qdrant, Weaviate, or Milvus.
• Fine-tune and deploy open-weight language models using LoRA, QLoRA, PyTorch, and distributed training frameworks.
• Develop and optimize Small Language Models (SLMs) for efficient cloud, on-premise, and edge deployment.
• Apply model training techniques including supervised fine-tuning, RLHF, DPO, distillation, and quantization.
• Explore multimodal AI solutions combining text, images, 3D data, and engineering documentation.

Engineering Intelligence & Simulation

• Integrate AI agents with CAD/BIM data, digital twins, and simulation environments.
• Develop hybrid AI solutions combining rules-based reasoning and machine learning.
• Implement optimization algorithms, graph search, constraint solving, and explainable decision-making.
• Explore world foundation models, including NVIDIA Cosmos, for simulation, synthetic data generation, and physical reasoning.

MLOps, AgentOps & Deployment

• Build and maintain scalable MLOps and AgentOps pipelines, including CI/CD, versioning, monitoring, and model evaluation.
• Implement observability and tracing using LangSmith, Langfuse, or similar tools.
• Establish AI guardrails, reliability mechanisms, and performance evaluation frameworks.
• Deploy secure AI solutions using Docker, Kubernetes, Linux, GPU cloud infrastructure, and private on-premise environments.
• Collaborate with multidisciplinary engineering teams to deliver reliable, production-ready AI solutions.

Location: Spain — Remote