AI Engineer/Architect – LLM, RAG & Applied AI
Knowit Aktiebolag (publ)
Stockholm, Stockholms län
Onsite
2026-06-14
Estimated salary · Stockholm
~ SEK 629,242 - SEK 873,525
Low
SEK 572K
Median
SEK 674K
High
SEK 800K
Market in Stockholm · SCB 2025
Estimated net pay
SEK 40,274 - SEK 50,545
/month · 23% withheld
after tax & contributions · on the estimated salary · Individual taxation — marital status and dependents do not affect it
Job description
We're building AI-native systems – and helping shape how AI transforms engineering, products and organisations.
At Knowit Connectivity, AI is becoming a fundamental part of how modern systems are designed, developed and operated. We're looking for experienced engineers and architects who want to help clients move beyond experimentation and build production-grade AI solutions that create real business value.
As we continue to invest in AI as a company, we're looking for people who want to combine deep technical expertise with a passion for shaping the future of AI-enabled engineering.
About the role
As a senior consultant, you'll work across the full lifecycle of applied AI – from strategy and architecture to implementation, deployment and continuous improvement.
You will help clients understand how AI changes products, workflows and technical systems while taking a leading role in designing solutions that are scalable, observable and trustworthy.
In addition to client engagements, you will be part of Knowit Connectivity's AI Advisory Team. The team plays a central role in shaping our AI strategy, developing internal capabilities, evaluating emerging technologies and helping drive our AI initiatives forward.
This means your role is divided between delivering value in client assignments and helping strengthen Knowit's AI capabilities internally. You'll collaborate with other senior AI specialists, architects and business leaders to identify opportunities, establish best practices and continuously evolve how we apply AI across the organisation.
Typical work includes:
Designing AI-native architectures and intelligent systems
Building and deploying LLM-based applications in production
Implementing RAG solutions across large and complex information landscapes
Designing agent-based workflows and autonomous system behaviours
Defining governance, observability and evaluation strategies for AI systems
Integrating AI capabilities into existing products, platforms an