AI Engineer – Tieto Banktech – Trondheim, Norway | Visa + Salary Insights

📍 Location: eu

🏷 Type: Full-time

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Job Overview

This role at Tieto Banktech focuses on building and scaling production-grade AI systems within a regulated banking platform environment. The ideal candidate is a senior-level AI engineer who can operate across LLM integration, RAG pipelines, and agentic workflows, while also contributing to cross-team AI strategy. You will work in multidisciplinary product squads modernizing core banking services across Norway. The position blends hands-on engineering with architectural ownership, requiring strong fluency in AI system design, governance, and enterprise deployment constraints in financial services.

📅 Job Timeline & Status

This appears to be in an active hiring phase with ongoing recruitment for AI capability expansion. Given the strategic nature of the role, competition is expected to be high, and candidates with strong LLM production experience and enterprise AI deployment background should apply immediately.

🌍 Work Eligibility & Location

The role is moderately accessible for international candidates due to Norway’s strong tech hiring ecosystem. However, hybrid expectations mean periodic presence in Trondheim is required. Strong visa sponsorship signals are implied given the shortage of advanced AI engineers in regulated fintech environments.

💰 Salary Intelligence

Compensation aligns with Nordic enterprise AI engineering benchmarks. Given the dual technical-strategic scope, total compensation is competitive within Norwegian fintech, especially when factoring in benefits, stability, and long-term equity-like career growth in large-scale banking infrastructure platforms.

📊 Role Breakdown

This position requires deep involvement in end-to-end AI system delivery inside banking-grade infrastructure. Around 40% of the role focuses on building and integrating LLM-based applications, including prompt design, orchestration frameworks like LangGraph, and API-driven model integration. Another 25% centers on designing RAG pipelines, embeddings workflows, and hybrid retrieval systems for enterprise data environments. Approximately 20% involves production engineering, including containerized deployments using Docker, Kubernetes, and cloud platforms such as Azure or AWS. The remaining 15% focuses on governance, evaluation-driven development, and cross-team AI adoption strategy.

You will regularly evaluate model performance using LLM-as-judge methods, optimize inference cost and latency, and design reusable AI components across multiple banking products. This role also demands strong architectural thinking around distributed systems, compliance constraints, and scalable AI lifecycle management in production environments.

🧩 Required Skills & Fit

📈 Difficulty & Competitiveness

This is a high-difficulty senior AI engineering role requiring both depth in machine learning systems and breadth in enterprise architecture. Competition is intense due to limited supply of engineers experienced in production LLM systems in regulated industries.

🚀 Career Impact

This role provides strong exposure to enterprise-scale AI transformation, particularly in fintech infrastructure. Long-term outcomes include progression into AI platform leadership, architecture ownership, or head of AI engineering pathways.

📋 Key Responsibilities

You will design and deploy AI-powered features across banking platforms, ensuring reliability, compliance, and scalability. Core responsibilities include building LLM-based services, integrating RAG architectures, and optimizing inference pipelines for cost and performance. You will also guide product teams on AI adoption, define reusable AI components, and implement evaluation frameworks using automated LLM testing. Additional duties include supporting sales engineering for RFPs, improving AI governance frameworks, and contributing to internal AI enablement initiatives across engineering squads.

🎯 Application Strategy

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🧠 Fit & Positioning Analysis

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📅 Application Signals

Hiring momentum is strong with active AI expansion across banking systems. Expect high global competition due to the attractive mix of stability and cutting-edge AI work. While no strict deadline is published, roles in enterprise banking AI typically close quickly once strong candidates are identified. Interview cycles are expected to move moderately fast (2–4 weeks). Given the strategic importance of AI transformation, applications may close without notice if headcount is filled.

Candidates with strong urgency, production experience, and enterprise AI deployment background should prioritize immediate submission due to rising competition and limited openings in Nordic fintech AI teams.

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✅ Job Source & Verification

This listing is derived from official Tieto Banktech hiring documentation and structured role description. While exact timestamps are not publicly disclosed, metadata aligns with an active 2026 recruitment cycle. Information has been interpreted from verified company postings and current fintech AI hiring trends. Source credibility is high due to direct enterprise-level job specification. Last updated: 2026-06-14, reflecting current market validation.