Logiciel Solutions | AI-Powered Teams That Feel In-House

AI Engineer

• Design and implement multi-step AI agents, reasoning flows, and automation workflows interacting with internal and external systems.
• Implement agent logic, tooling, and workflow orchestration.
• Develop retrieval pipelines and knowledge-grounding workflows.
• Build structured extraction flows using LLMs and transformation logic.
• Convert prototypes into production-ready services with monitoring and predictable behavior.
• Ensure agents are explainable, observable, and have defined failure and fallback paths.
• Define architectures for applied AI solutions using TypeScript/Node or Python.
• Integrate agents with APIs, databases, event systems, and existing product surfaces, including protocols such as MCP or A2A.
• Establish patterns for prompt management, model selection, retrieval, and verification.
• Implement evaluation pipelines to monitor accuracy, drift, and reliability.
• Build reusable modules and internal tooling to accelerate future AI development.
• Build functional prototypes and demos to validate ideas.
• Assess feasibility, complexity, and risks of proposed AI features.
• Collaborate with sales and delivery teams on scope and technical approach.
• Advise on trade-offs between model and architecture choices.
• Help create realistic timelines and delivery plans.
• Mentor backend engineers on AI patterns, workflows, and integration techniques.
• Introduce practices for testing, evaluation, observability, and versioning.
• Share knowledge on prompting, model behavior, and tool usage.
• Establish standards for applied AI solution design and delivery.

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AI Engineer, Senior