LLM Engineer - AI Agent Development

Nimber; Maia; Presencial; EUR 30 000 - 50 000; Tempo integral

Resumo da oferta

A pioneering AI company in Maia seeks an LLM Engineer to join their AI development team. You will enhance agent capabilities and architect solutions for intelligent agents using tools like LangChain and LangGraph. This full-time, on-site role requires proficiency in Python and a passion for AI/ML. If you're ready to shape the future with innovative technologies, apply now.

Serviços

Competitive salary based on experience

Budget for AI/ML courses and certifications

Access to premium LLM APIs and dev tools

Modern office with high-performance hardware

Flexible working hours

Complementary coffee and snacks

Qualificações

• Experience with LLM APIs (OpenAI GPT-4, Claude).
• Familiarity with vector databases (PostgreSQL + pgvector).
• Knowledge of AI/ML frameworks and tools.

Responsabilidades

• Design workflows with LangGraph.
• Build pipelines for document retrieval.
• Implement hallucination control mechanisms.
• Develop agent architectures for high concurrency.

Conhecimentos

LangChain

Python asyncio

Problem-solving

Multi-agent systems

Load testing

Data-driven decision making

Empathy in user experience

Formação académica

Bachelor's degree in Computer Science or related field

Ferramentas

PostgreSQL

Docker

FastAPI

Redis

AWS

Descrição da oferta de emprego

Hello! Do you believe you found this opportunity by chance?
At Nimber, we dont believe in chance we believe in curiosity, creativity, and people who build with purpose.

If this role sparked your interest, great. Keep reading

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.