Agent / LLM Engineer - AI Agent Development**Location:** Maia, Porto, Portugal (On-site)**Experience Level:** Junior to Mid Level (1-4 years)**Employment Type:** Full-timeAbout the RoleWe are seeking a passionate and innovative Agent/LLM Engineer to join our AI development team. You will be responsible for refining and enhancing our Agents and developing the next-generation with MCP (Model Context Protocol) server implementation. This is an exciting opportunity to work at the forefront of AI agent technology and contribute to cutting-edge customer support solutions.Key ProjectsCurrent: Backoffice Agent Refinement
* Enhance existing agent capabilities and performance
* Optimize agent workflows using Lang Graph for complex multi-step reasoning
* Implement advanced hallucination detection and mitigation strategies
* Improve agent reliability, error handling, and output validation mechanismsUpcoming: Frontend Agent & MCP Server
* Design and develop customer-facing agents with real-time capabilities
* Implement MCP (Model Context Protocol) server architecture for seamless model integration
* Create scalable agent infrastructure supporting concurrent user interactions
* Develop robust conversation management and context preservation systemsKey Responsibilities### AI Agent Development & Architecture
* Design and implement complex agent workflows using **Lang Graph** for state management
* Build robust **Lang Chain** pipelines for document processing and retrieval
* Develop **hallucination detection and control** mechanisms to ensure response accuracy
* Create **scalable agent architectures** supporting high-concurrent user loads
* Implement **RAG (Retrieval-Augmented Generation)** systems with vector databases### Advanced Agent Capabilities
* Develop **multi-agent orchestration** and coordination systems
* Implement **tool calling and function execution** frameworks
* Create **memory management systems** for long-term conversation context
* Build **agent evaluation and testing** frameworks for quality assurance
* Design **prompt optimization** and dynamic prompt generation systems### Production & Scalability
* Implement **horizontal scaling** strategies for agent workloads
* Develop **caching mechanisms** for frequently accessed information
* Create **load balancing** solutions for distributed agent processing
* Monitor and optimize **token usage and cost management**
* Implement **rate limiting and abuse prevention** mechanisms### Quality & Safety
* Build **output validation pipelines** to catch and correct hallucinations
* Implement **safety filters** and content moderation systems
* Develop **confidence scoring** and uncertainty quantification
* Create **human-in-the-loop** workflows for critical decisions
* Design **audit trails** and conversation logging systems## Required Technical Skills### AI/ML Frameworks & Tools
* **Lang Chain:** Advanced experience building complex agent pipelines and chains
* **Lang Graph:** Proficiency in creating stateful, cyclical agent workflows
* **Vector Databases:** Experience with Postgre SQL with pgvector
* **LLM APIs:** Integration with Open AI GPT-4, Anthropic Claude, and other LLM providers
* **Embeddings:** Working with text embeddings for semantic search and retrievalHallucination Control & Validation
* **Fact-checking mechanisms:** Building verification systems against knowledge bases
* **Confidence scoring:** Implementing uncertainty quantification for LLM outputs
* **Output validation:** Creating structured validation pipelines
* **Guardrails:** Implementing safety rails and content filtering
* **Ground truth verification:** Comparing outputs against authoritative sources### Scalability & Performance
* **Async programming:** Python asyncio for concurrent request handling
* **Queue systems:** Redis, Celery, or RQ for background task processing
* **Caching strategies:** Redis/Memcached for response and embedding caching
* **Database optimization:** Query optimization and connection pooling
* **Load testing:** Performance testing for high-concurrency scenarios### Backend Development
* **Python:** Strong proficiency (2+ years) with modern async frameworks
* **Fast API/Flask:** Building robust APIs with proper error handling
* **Database Integration:** Postgre SQL, vector databases, and ORM usage
* **Authentication:** JWT, OAuth, and secure API design
* **Docker/Containerization:** Containerized deployment and orchestration## Advanced Skills (Preferred)### Agent Architecture Patterns
* **Multi-agent systems:** Coordinating multiple specialized agents
* **Tool use frameworks:** Re Act, Plan-and-Execute, and custom reasoning patterns
* **Context window management:** Efficient handling of large conversation contexts
* **Streaming responses:** Real-time response generation and Web Socket integration
* **Agent memory architectures:** Short-term, long-term, and episodic memory systems### ML/AI Operations
* **Model monitoring:** Tracking agent performance and behavior drift
* **A/B testing:** Experiment frameworks for agent improvements
* **Data pipeline management:** ETL for training data and knowledge base updates
* **Model fine-tuning:** Custom model adaptation for specific use cases
* **MLOps practices:** Version control for prompts, models, and agent configurations### Integration & Protocols
* **MCP (Model Context Protocol):** Implementation and server development
* **Web Socket/SSE:** Real-time bidirectional communication
* **Microservices:** Agent deployment in distributed architectures
* **API Gateway patterns:** Request routing and transformation
* **Event-driven architecture:** Pub/sub patterns for agent communication## Soft Skills & Attributes- **Problem-solving mindset:** Creative approaches to complex AI challenges
* **Attention to detail:** Precision in prompt engineering and output validation
* **User empathy:** Understanding customer needs in conversational interfaces
* **Analytical thinking:** Data-driven approach to agent performance optimization
* **Communication skills:** Excellent English and Portuguese for team collaboration
* **Adaptability:** Comfort with rapidly evolving AI technologies and best practices
* **Quality focus:** Commitment to building reliable, production-ready AI systems## What We Offer- Competitive salary commensurate with experience level
* Professional development budget for AI/ML courses and certifications
* Access to premium LLM APIs and latest AI development tools
* Modern office environment in Maia, Porto with powerful development hardware
* Flexible working hours within core business hours
* Opportunity to work with cutting-edge AI agent technologies
* Coffe and snacks## Work EnvironmentThis is an **on-site position** based in our Maia office. We foster a collaborative environment where AI engineers can experiment, share discoveries, and iterate quickly on agent improvements. Our team values continuous learning and staying at the forefront of AI agent development.## Technical Stack- **AI/ML:** Lang Chain, Lang Graph, Open AI GPT-4, Anthropic Claude, Hugging Face
* **Languages:** Python (primary), Type Script/Java Script (frontend integration)
* **Databases:** Postgre SQL with pgvector, Redis for caching
* **Infrastructure:** Docker, Kubernetes, cloud deployment (AWS/GCP/Azure)
* **Monitoring:** Custom agent analytics, Open Telemetry, Prometheus
* **Development:** Fast API, Pydantic, pytest, black, mypy## Technical Interview TopicsCandidates should be prepared to discuss:### Agent Architecture & Development
* Lang Graph workflow design for complex multi-step reasoning
* Lang Chain pipeline optimization and best practices
* RAG system architecture and vector database selection
* Multi-agent coordination and communication patterns### Quality & Safety
* Hallucination detection and mitigation strategies
* Output validation and fact-checking mechanisms
* Confidence scoring and uncertainty quantification
* Safety filters and content moderation approaches### Scalability & Performance
* Horizontal scaling strategies for agent workloads
* Caching mechanisms for embeddings and responses
* Async programming patterns for concurrent users
* Token optimization and cost management strategies### Integration & Production
* MCP protocol implementation approaches
* Real-time conversation management systems
* Error handling and graceful degradation patterns
* Monitoring and observability for AI agents## Sample Technical Challenges