Senior LLM Inference Engineer / AI Infrastructure Architect - 20159

About this job

Veritaz is a leading IT staffing solutions provider in Sweden, committed to advancing individual careers and aiding employers in ensuring the perfect talent fit. With a proven track record of successful partnerships with top companies, we have rapidly grown our presence in the USA, Europe, and Sweden as a dependable and trusted resource within the IT industry. Job Overview Role Summary An experienced Senior LLM Inference Engineer / AI Infrastructure Architect Key Responsibilities Analyze existing LLM-powered applications and agent workflows to identify performance bottlenecks, resource constraints, and cost drivers. Optimize inference performance with respect to token usage, response times, concurrency, GPU memory utilization, and overall service capacity. Evaluate model selection, including the use of smaller models for less complex tasks where appropriate. Assess and implement inference optimization techniques such as prompt caching, batching, and quantization. Conduct performance measurement, load testing, and capacity planning for AI services. Compare operating models including model APIs, self-hosted cloud deployments, on-premises infrastructure, and hybrid alternatives. Develop a concrete decision basis for future operating strategy, including recommendations for GPU capacity, scaling approaches, and economically viable usage-volume thresholds. Document findings, implemented improvements, and technical recommendations, and transfer knowledge to the development team. Technical Stack & Requirements Production deployment and optimization of Large Language Models (LLMs). Strong understanding of the relationship between model size, context length, GPU memory, concurrency, response latency, and operating cost. Hands-on experience with Python, Linux, and container-based deployments. Experience with LLM inference frameworks such as vLLM, TensorRT-LLM, or equivalent technologies. Experience with performance benchmarking, load testing, and capacity planning for AI solutions. Practical knowledge of caching, batching, quantization, and GPU-oriented inference optimization. Qualifications & Experience The ideal candidate has substantial practical experience deploying and optimizing language models in production environments. They can translate technical analysis and performance results into clear, commercially relevant recommendations and decision material. Experience with AI agents and agent-based workflows is advantageous. Additional merit is given for experience with distributed inference, multi-GPU or multi-node scaling, and evaluating cloud, self-hosted, and hybrid operating models from both technical and financial perspectives.

Want to know if you're a fit? Check your CV against this role — free, in 30 seconds.

Most-requested skills in Sweden

Based on 339 Sweden vacancies that list requirements, these are the skills employers ask for most often.

• CAD20% of postings
• FEM/FEA18% of postings
• PLC13% of postings
• MATLAB12% of postings
• Lean12% of postings
• Project Management12% of postings
• Quality Assurance9% of postings
• CATIA8% of postings

Similar jobs

• VVS-ingenjör · Hagmans Nordic AB
• Staff Engineer – Product & Tech to Lendo · TNG Group AB
• Senior Frontend Engineer – Product & Tech to Lendo · TNG Group AB
• Senior Backend Engineer – Product & Tech to Lendo · TNG Group AB
• Projektkvalitetsingenjör till kund inom försvarssektorn · Aura Personal AB
• Forskningsingenjör i utveckling av hjärninspirerad artificiell intelligens · Kungliga Tekniska Högskolan