Company Description
We are an Engineering and Product Development Center that designs, develops, and produces new technologies, products, and services for a more sustainable society.
We work by bridging the gap between cities, industry, and universities around large-scale development and industrialization programs for products and services, originating in Portugal, in high-tech sectors such as mobility, aeronautics, and space.
The AI Engineering area is the new layer of intelligence in the CEiiA ecosystem. It reports directly to the board and its mission is to give autonomy to the systems that CEiiA builds.
This AI Engineer (m/f/d) position will be part of the AI Engineering area.
Job Description
You will work directly with the Head of AI Engineering and the Lead AI Engineer.
The projects are real and already underway. You won't be maintaining legacy code. You'll be building things that don't exist—agents that reason, pipelines that decide, systems that anticipate. And you'll ensure that the data on which these things are based is robust, sovereign, and reliable.
Responsibilities:
- Design and maintain the AI Engineering data architecture — how data arrives, how it is stored, how it is versioned, and who has access to it;
- Ensuring sovereignty: critical data does not leave the perimeter;
- Curate and structure messy data from multiple sources;
- Build ingestion, transformation, and quality pipelines — clean data before it reaches the model;
- Structuring data for reasoning: knowledge graphs, embeddings, RAGS, context injection;
- Architecting, designing, and building intelligent systems on LLMs for specific domains;
- Designing how the machine thinks: cognitive architectures, knowledge representation, prompt architecture, tool ecosystems, context, and memory;
- Orchestrating models — frontier, open-source, fine-tuned — with reasoned thinking about cost, latency, quality, and privacy;
- Prototype quickly (in days, not months) and turn PoCs into a product;
- Failure design — brains hallucinate, lose context, become confused. Design guardrails, fallback, graceful degradation;
- Eval mindset — how do you know the brain has improved? Designing evals, red-teaming, testing edge cases, measuring reasoning quality — not just accuracy.
Qualifications
- Bachelor's degree in Computer Engineering, Systems Engineering, Mathematics, Physics, or equivalent technical field;
- 3 or more years of experience, with at least 2 years in AI/ML systems in production (products that work with real users)
- Data architecture for AI — structuring a sovereign data layer: ingestion, storage, quality, versioning, access. It's not a nice-to-have, it's the starting point;
- Solid proficiency in at least one backend language (Python, Go, Rust, or equivalent), agents, integration, and complex pipelines;
- Real-world experience with LLMs in production: tool-calling, structured outputs, context management, cost and latency management;
- Agent architecture: systems with multi-step reasoning, memory, tool ecosystems, fallback;
- Model orchestration — when to use frontier vs open-source vs fine-tuned, with clear trade-offs;
- AI infrastructure: GPUs, model serving (vLLM, TGI or equivalent), inference pipelines, compute cloud vs on-premise;
- Integration of data from multiple sources;
- Genuine curiosity;
- Systems and critical thinking;
- Comfort with ambiguity;
- Clear communication for non-technical profiles;
- Portuguese (required) + technical English.
We also value:
- LLMOps — model versioning, monitoring, deployment, eval pipelines;
- GCP, Docker;
- Background in complex domains: defense, space, energy, critical infrastructure.
Additional Information
- Relaxed work environment, dynamic and multidisciplinary teams.
- We facilitate and promote a balanced and healthy lifestyle, integrating work with personal life.
- Health insurance.
- Partnerships with gyms, Nutrium, among others.
- Up to three additional vacation days.
- Free birthday.