LLM Engineer AI Agent Development

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Were not just hiring were building a team of AI pioneers ready to shape the future of intelligent agents. If youre passionate about LLMs agent architecture and solving complex problems with cutting-edge tools this is your moment.

Position: LLM Engineer - AI Agent Development

Location: Maia Porto (On-site)
Experience Level: Junior to Mid (1-4 years)
Employment Type: Full-time

About the Role

Youll join our AI development team to refine existing agents and help build the next generation using MCP (Model Context Protocol). From hallucination control to multi-agent orchestration youll be working at the forefront of conversational AI and customer support automation.

Key Projects

Current: Backoffice Agent Refinement

• Enhance agent capabilities and performance
• Optimize workflows using LangGraph for multi-step reasoning
• Implement hallucination detection and mitigation strategies
• Improve reliability error handling and output validation

Upcoming: Frontend Agent & MCP Server

• Design real-time customer-facing agents
• Implement MCP server architecture for seamless model integration
• Build scalable infrastructure for concurrent interactions
• Develop robust conversation management and context preservation

Key Responsibilities

AI Agent Development & Architecture

• Design complex workflows with LangGraph
• Build LangChain pipelines for document retrieval
• Implement hallucination control and validation mechanisms
• Create scalable agent architectures for high concurrency
• Develop RAG systems with vector databases

Advanced Agent Capabilities

• Orchestrate multi-agent systems
• Implement tool calling and function execution frameworks
• Design memory systems for long-term context
• Build agent evaluation and testing frameworks
• Optimize prompts and dynamic generation strategies

Production & Scalability

• Apply horizontal scaling strategies
• Develop caching for embeddings and responses
• Implement load balancing and token cost optimization
• Build rate limiting and abuse prevention mechanisms

Quality & Safety

• Create output validation pipelines
• Implement safety filters and content moderation
• Develop confidence scoring and uncertainty quantification
• Design human-in-the-loop workflows
• Build audit trails and logging systems

Required Technical Skills

AI/ML Frameworks & Tools

• LangChain & LangGraph for agent workflows
• Vector databases (PostgreSQL pgvector)
• LLM APIs (OpenAI GPT-4 Claude etc.)
• Embeddings for semantic search and retrieval

Hallucination Control & Validation

• Fact-checking and ground truth verification
• Confidence scoring and output validation
• Guardrails and content filtering

Scalability & Performance

• Async programming with Python asyncio
• Queue systems (Redis Celery RQ)
• Caching strategies and database optimization
• Load testing for high-concurrency scenarios

Backend Development

• Python (2 years) FastAPI or Flask
• PostgreSQL vector DBs ORM usage
• Authentication (JWT OAuth)
• Docker and container orchestration

Soft Skills & Attributes

• Creative problem-solving mindset
• Precision in prompt engineering and validation
• Empathy for user experience in conversational interfaces
• Analytical thinking and data-driven decision-making
• Strong communication in English and Portuguese
• Adaptability to fast-evolving AI technologies
• Commitment to quality and production-readiness

What We Offer

• Competitive salary based on experience
• Budget for AI/ML courses and certifications
• Access to premium LLM APIs and dev tools
• Modern office in Maia with high-performance hardware
• Flexible working hours within core business times
• Opportunity to work on cutting-edge AI agent tech
• Coffee and snacks always available

Work Environment

This is an on-site position in our Maia office. We foster a collaborative space where AI engineers experiment share discoveries and iterate quickly. Our team thrives on continuous learning and pushing the boundaries of agent development.

Technical Stack

• AI/ML: LangChain LangGraph GPT-4 Claude Hugging Face
• Languages: Python (primary) TypeScript/JavaScript (frontend)
• Databases: PostgreSQL pgvector Redis
• Infrastructure: Docker Kubernetes AWS/GCP/Azure
• Monitoring: OpenTelemetry Prometheus
• Development: FastAPI Pydantic pytest black mypy

Technical Interview Topics

Agent Architecture & Development

• LangGraph workflow design
• LangChain pipeline optimization
• RAG architecture and vector DB selection
• Multi-agent coordination patterns

Quality & Safety

• Hallucination detection and mitigation
• Output validation and fact-checking
• Confidence scoring and safety filters

Scalability & Performance

• Horizontal scaling and caching
• Async programming for concurrency
• Token optimization and cost control

Integration & Production

• MCP protocol implementation
• Real-time conversation systems
• Error handling and observability

Sample Technical Challenges

You may be asked to:

• Design a LangGraph workflow for a multi-step support scenario
• Implement hallucination detection for agent responses
• Architect a system for 1000 concurrent conversations
• Debug inconsistent agent outputs
• Optimize a RAG system for speed and accuracy

Ready to stop being just another number
Apply now and become a Nimber where your ideas shape the future of AI.