Lead Agentic AI Engineer (Remote Europe) | GenAI, RAG & LLM Jobs

📍 Location: eu

🏷 Type: Not specified

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Job Overview

This role is a Senior-Level opportunity focused on building agentic AI systems within the logistics and transportation domain. You will lead the design and deployment of Generative AI architectures, driving innovation in how freight moves globally. The ideal candidate brings 8–15 years of engineering experience, deep expertise in LLMs, RAG pipelines, and distributed systems, and the ability to architect scalable, production-grade AI solutions.

🌍 Work Eligibility & Location

This is a Remote role spanning multiple European countries, enabling strong international accessibility. However, lack of clear visa sponsorship signals suggests preference for candidates already authorized to work in Europe. Timezone alignment is important due to cross-regional collaboration.

💰 Salary Intelligence

The estimated range reflects compensation typical for Senior-Level AI engineering roles in Europe, particularly those involving LLM systems and architecture ownership. While not top-tier FAANG-level, this is competitive within the logistics AI sector, especially given the strategic ownership and leadership scope.

📊 Role Breakdown

This position centers on architecting and scaling agentic AI systems for real-world logistics applications. Approximately 35% of the role involves designing system architecture, including agent orchestration graphs, RAG pipelines, and distributed workflows. Another 25% focuses on hands-on development using Python, integrating LLMs, and building production-ready pipelines. Around 20% is dedicated to evaluation and experimentation, including RLHF variants, benchmarking, and optimizing accuracy and latency. The remaining 20% involves leadership responsibilities such as mentoring engineers, reviewing code, and defining engineering standards. Key actions include scaling AI systems, optimizing inference pipelines, and ensuring production reliability through observability and CI/CD integration.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This role has a high difficulty level due to its requirement for deep specialization in LLM systems combined with architecture ownership. The 8–15 years experience barrier filters out most candidates, making this highly competitive among senior engineers. Candidates must demonstrate both technical depth and strategic thinking.

🚀 Career Impact

This role delivers strong career acceleration by positioning you at the forefront of agentic AI systems. You gain exposure to real-world AI deployment at scale, which is critical for transitioning into AI leadership roles. The combination of architecture ownership and domain impact significantly enhances long-term positioning.

📋 Key Responsibilities

You will architect and deploy scalable GenAI systems, define agent orchestration patterns, and build RAG pipelines for production use. Core tasks include designing CI/CD pipelines for AI systems, implementing observability tools like LangSmith, and optimizing model performance across accuracy, latency, and cost. You will also lead experimentation with RLHF techniques and retrieval strategies, while ensuring system reliability. Additionally, you will mentor engineers, enforce secure-by-design practices, and review code to maintain high engineering standards.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

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🧠 Fit & Positioning Analysis

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📅 Application Signals

This role shows high urgency due to recent posting and strategic importance. Expect strong competition from senior AI engineers with production LLM experience. Early application significantly improves visibility. Candidates with demonstrated real-world deployments will stand out quickly.

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✅ Job Source & Verification

This job is sourced directly from the company’s official careers page, ensuring high source credibility. The listing was last updated recently, indicating active hiring. All details reflect the latest available information, making this a reliable opportunity for qualified candidates.