Team Lead AI Technology & Tools Job Sweden (HPC, MLOps)

📍 Location: eu

🏷 Type: Not specified

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Job Overview

This is a Senior-Level AI leadership role focused on HPC-based AI infrastructure, MLOps, and distributed systems within Europe’s flagship AI initiatives. Hosted by Linköping University under NAISS, the position targets candidates with 5+ years experience in large-scale AI systems and GPU environments. You will lead technical strategy and execution across cloud-native AI platforms, collaborating with research institutions, startups, and public-sector partners to deploy production-ready AI solutions.

🌍 Work Eligibility & Location

🌍 Visa Sponsorship: Not explicitly stated (possible for high-skilled candidates)
✈️ Relocation Support: Not specified
🏠 Remote Type: Hybrid (up to 40% remote)
⏰ Timezone Requirement: European working hours
🌐 Country Restrictions: Must be eligible to work in Sweden/EU
🗣️ Language Requirement: English (professional level)

This role is primarily Hybrid, based in Sweden, with flexibility for remote work up to 40%. While visa sponsorship is not explicitly confirmed, candidates with highly specialized AI/HPC expertise may be considered. International applicants should evaluate EU work eligibility or prepare for sponsorship discussions early in the process.

💰 Salary Intelligence

💰 Official Salary: Not disclosed
📊 Estimated Range: €75,000 – €105,000/year
📈 Level: Senior-Level

While the official salary is not published, comparable Senior AI leadership roles in Europe suggest a range of €75,000 – €105,000, depending on experience and research credentials. Given the strategic nature of this role within EuroHPC and national AI infrastructure, compensation is competitive within the public-sector research ecosystem, often complemented by strong benefits and long-term career stability.

📊 Role Breakdown

This position blends technical leadership (40%), hands-on AI system development (30%), and cross-organizational collaboration (30%). You will lead the design and optimization of AI workflows on GPU clusters, HPC systems, and distributed training frameworks, ensuring efficient scaling and performance. A major focus is enabling end-to-end MLOps pipelines using cloud-native architectures, including containerized environments and federated AI services.

You will guide teams in deploying AI models across hybrid infrastructures, integrating solutions into real-world applications spanning academia, startups, and public sector organizations. Expect to work deeply with distributed systems, Kubernetes, PyTorch, and large-scale data pipelines. Additionally, you will play a critical role in translating technical capabilities into applied AI use cases, ensuring that infrastructure investments lead to tangible outcomes.

This role requires balancing strategic system design with operational execution, making it ideal for candidates who thrive in complex, high-impact environments.

🧩 Required Skills & Fit

✅ Must: PhD or equivalent experience in AI, ML, or HPC
✅ Must: Proven experience with GPU-based training and distributed AI systems
✅ Must: Strong background in MLOps, pipelines, and production AI workflows
➕ Bonus: Experience with LLMs, generative AI, or speech AI
➕ Bonus: Knowledge of cloud-native environments and containerization

📈 Difficulty & Competitiveness

⚡ Level: High
📊 Experience barrier: 5+ years
🧠 Skill complexity: Advanced (HPC + AI + MLOps)
🌍 Competition: Global, research-heavy talent pool

This is a high-difficulty role due to the combination of advanced AI systems, HPC infrastructure, and leadership expectations. Candidates must demonstrate 5+ years of relevant experience, often including research or production-scale AI deployments. Competition is intense, especially from candidates with EuroHPC, academia, or deep-tech backgrounds.

🚀 Career Impact

⭐ Impact Rating: ⭐⭐⭐⭐☆
🏢 Brand value: Strong European research infrastructure
📚 Skill growth: HPC, distributed AI, federated systems
🚀 Future opportunities: AI infrastructure leadership, research director roles

This role offers significant long-term career leverage, positioning you within Europe’s core AI infrastructure ecosystem. You will gain deep expertise in HPC-driven AI systems and exposure to large-scale, cross-border initiatives. This experience directly unlocks future leadership roles in AI platforms, research organizations, and advanced industrial AI environments.

📋 Key Responsibilities

You will lead the development of AI workflows on HPC and GPU systems, ensuring scalability and efficiency. A core responsibility is to design and optimize distributed training pipelines using technologies like PyTorch, Kubernetes, and containerized environments. You will also implement MLOps practices, including versioning, reproducibility, and lifecycle management.

Additionally, you will collaborate with researchers and industry partners to integrate AI solutions into real-world use cases. This includes bridging infrastructure with applications and supporting hybrid and federated computing environments. Expect to translate technical capabilities into deployable solutions while maintaining performance and reliability standards.

🎯 Application Strategy

🎯 Best apply method: Apply via official university portal with tailored CV
🔥 Highlight: Experience with distributed AI training
🔥 Highlight: Leadership in MLOps or AI platform development
❌ Avoid: Generic AI experience without scale or infrastructure context
❌ Avoid: Lack of measurable impact in previous roles

🧠 Application Optimization (Adaptive)

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Senior AI Team Lead working on HPC infrastructure, MLOps pipelines, and distributed AI systems within EuroHPC initiatives.

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📅 Application Signals

This role has a fixed deadline (April 30, 2026), indicating high urgency for applicants. Given the strategic importance and seniority, expect strong competition from both academic and industry candidates across Europe. Early application significantly improves visibility and consideration.

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Tailoring your CV to this exact role significantly increases your chances.

HPC AI Systems: Demonstrate GPU cluster training and large-scale model optimization

Distributed Training: Highlight PyTorch distributed workflows and multi-node scaling experience

MLOps Pipelines: Showcase end-to-end pipelines with versioning, CI/CD, reproducibility metrics

Cloud-Native Infrastructure: Detail Kubernetes, containers, hybrid HPC-cloud integrations delivered

Leadership Impact: Quantify team leadership, cross-organization AI deployments, measurable outcomes

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✅ Job Source & Verification

This job listing originates from Linköping University’s official careers page, ensuring high source credibility. The position is part of NAISS and the EuroHPC initiative, confirming its authenticity and strategic importance. Last updated: April 2026, with an active application deadline, making it a current and verified opportunity.