AI Developer

Job Title: AI Developer Location: Stockholm, Sweden Hybrid 2days office per week Job type: Contract Required Technical Skill Set** AI-powered applications and agentic workflows, Model Context Protocol (MCP) servers, RAG (Retrieval-Augmented Generation) solutions Technical Competency Needed:

• Generative AI and Large Language Models (LLMs)
• Python and AI/ML application development
• Agentic AI and multi-agent architectures
• Model Context Protocol (MCP) – servers, tools, resources, and integrations
• LLM orchestration frameworks and agent workflows
• Prompt engineering and context management
• Tool/function calling and API integrations
• Retrieval-Augmented Generation (RAG)
• REST APIs, databases, SaaS platforms, and enterprise systems
• AI application architecture and scalable cloud services
• LLM evaluation, monitoring, logging, and observability
• Automated testing and AI/agent evaluation frameworks
• Security, performance, reliability, and cost optimization

Eligibility Criteria:

• This Eligibility Criteria applicable if matching profile found in Sweden - Security clearance eligible for national security & autonomy requirements - Swedish residency of continuous 3+ years / SE citizenship

Responsibilities: • Design, develop, and integrate AI-powered applications and agentic workflows using modern LLM technologies. • Build and maintain Model Context Protocol (MCP) servers, tools, resources, and integrations to enable AI agents to interact with enterprise systems. • Develop orchestration frameworks and multi-agent workflows for coordinating AI agents, tools, APIs, and business processes. • Integrate LLMs with REST APIs, databases, SaaS platforms, internal services, and external tools through secure and scalable architectures. • Implement prompt engineering, tool/function calling, context management, memory, and RAG (Retrieval-Augmented Generation) solutions. • Develop production-grade AI services with strong focus on scalability, reliability, observability, security, and performance. • Work with engineering and product teams to translate business requirements into AI/agent-based solutions and technical architectures. • Evaluate and optimize LLM models, agent performance, prompts, workflows, latency, and operational costs. • Build automated testing, monitoring, logging, and evaluation frameworks to ensure quality and reliability of AI agents and MCP integrations. • Stay current with emerging GenAI, MCP, agent orchestration, LLM, and AI engineering technologies, and contribute to best practices and technical standards.