Tech Lead LLM & Generative AI – Norway (Remote) | Visa + Salary Insights

📍 Location: eu

🏷 Type: Full-time

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Job Overview

EverAI is hiring a Senior-Level Tech Lead (8+ years) to lead its LLM & Generative AI engineering function powering a large-scale AI companionship platform with tens of millions of users. The role focuses on production-grade model development, including fine-tuning, alignment, inference optimization, and moderation systems at extreme scale. You will operate at the intersection of applied LLM engineering, RLHF/DPO pipelines, and real-time conversational systems. This position suits an experienced AI engineer who thrives in high-velocity environments, is comfortable shipping model changes directly to production, and can balance innovation with safety constraints in complex conversational domains.

📅 Job Timeline & Status

This role is in an active hiring cycle with high engineering urgency due to rapid scaling demands. Given the seniority level and the niche LLM expertise required, applicants are advised to apply immediately. Competition is expected to be strong as the role sits at the core of a high-growth AI product. The pipeline is likely moving fast with technical screenings prioritized over long review cycles.

🌍 Work Eligibility & Location

This is a globally accessible Remote role, enabling candidates from multiple regions to apply. While Norway is the nominal hub, the organization operates with distributed engineering teams. Senior candidates with strong LLM shipping experience are prioritized regardless of location, although European time alignment may improve collaboration efficiency.

💰 Salary Intelligence

Compensation is competitive within the applied LLM infrastructure market, reflecting the seniority and production ownership expected. The estimated range aligns with high-growth AI startups operating at scale, especially those handling large token throughput and real-time inference systems.

📊 Role Breakdown

This role centers on building and scaling production LLM systems that support millions of concurrent users. You will design and optimize LLM inference pipelines using tools such as PyTorch, HuggingFace, and vLLM, ensuring low latency and high throughput for conversational AI workloads. A major focus is improving context handling, memory retrieval systems, and RAG architectures to maintain coherent long-form interactions.

You will also own model alignment strategies using SFT, RLHF, and DPO, deciding when to fine-tune versus re-architect pipelines. Approximately 40% of effort will involve production optimization, 30% model training and evaluation, and 30% data engine and moderation systems. You will design classifiers capable of nuanced moderation in complex conversational environments, replacing rigid filters with adaptive safety layers.

The system processes over 80M tokens daily, requiring strong engineering discipline in scalability, distributed inference, and latency reduction. You will also mentor a small AI engineering team, ensuring code quality, experimentation velocity, and measurable model improvements across releases.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a highly selective role requiring deep production AI expertise. Candidates must demonstrate not only model knowledge but also real-world systems engineering at scale. The combination of LLM alignment, infra optimization, and moderation complexity places this role at the upper tier of AI engineering difficulty.

🚀 Career Impact

This role provides significant exposure to frontier-scale AI systems. Engineers gain deep expertise in production LLM deployment, alignment engineering, and real-time AI systems. The experience is directly transferable to leading AI teams at top-tier global tech companies or founding AI-native startups.

📋 Key Responsibilities

You will lead the design and implementation of LLM-driven conversational systems, ensuring reliability at scale. Responsibilities include building and optimizing inference pipelines, improving context window management, and developing RAG architectures for enhanced memory performance. You will also manage the full model lifecycle, including dataset curation, fine-tuning, evaluation, and deployment.

A critical responsibility is designing advanced moderation systems capable of handling sensitive conversational content without degrading user experience. You will also mentor engineers, review architecture decisions, and ensure production stability across high-throughput systems processing tens of millions of tokens daily.

🎯 Application Strategy

🧠 Application Optimization (Adaptive)

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How to use: Paste into ChatGPT, Claude, or Gemini

🧠 Fit & Positioning Analysis

Evaluate your match before applying.

📅 Application Signals

This role shows strong hiring momentum with likely fast-moving interviews due to infrastructure scaling demands. Application urgency is high, and candidates with strong LLM deployment backgrounds are prioritized. Given the niche skill set, applicant volume is expected to be competitive globally, especially from senior AI engineers.

Deadline proximity suggests rolling review with no fixed closure. Interview speed expectations are relatively fast (1–2 stages technical-first). High-traffic visibility among AI professionals increases competition significantly.

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✅ Job Source & Verification

This listing is derived from an official EverAI posting in the AI engineering domain. Source credibility is high due to direct company-level disclosure and detailed technical breakdown of responsibilities. Last updated on June 14, 2026 during verification of role scope and compensation estimates. Information reflects current publicly available job description with inferred timeline and salary benchmarking based on comparable LLM Tech Lead roles in high-growth AI startups.