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AI Adoption & Artificial Intelligence Specialist Defense
Göteborg
Source: Arbetsformedlingen Platsbanken · original listing ↗
Source date: 11/6/2026
About this role
🚀 We’re Hiring: AI Adoption & Optimisation Specialist – Software Development (PRO4) Company: Hykmann Global Business Solutions AB Domain: Generative AI | AI-Assisted Software Development | Software Engineering Are you passionate about leveraging Generative AI to transform software development, improve engineering productivity, and establish effective AI-driven development practices? Hykmann Global Business Solutions AB is looking for an experienced AI Adoption & Optimisation Specialist to help an organisation developing safety-critical air traffic management systems maximise the benefits of AI-assisted software development. About the Role The organisation has already introduced Generative AI tools, primarily GitHub Copilot, across approximately 100 developers working at multiple locations. However, AI adoption, utilisation, and practical experience vary significantly between teams and individuals. The objective is to identify how AI can deliver measurable improvements in productivity, software quality, and engineering efficiency while maintaining security, competence, and cost-effectiveness. You will assess current AI adoption, establish performance indicators, identify optimisation opportunities, and recommend effective approaches to integrating AI throughout the software development lifecycle. Key Responsibilities 1. AI Adoption & Current-State Assessment Analyse existing usage of GitHub Copilot, LLMs, and other AI development tools. Identify differences in AI adoption and utilisation across teams. Understand low, average, and high AI consumption patterns. Evaluate the effectiveness and business value of existing AI solutions. Identify opportunities to improve adoption and engineering efficiency. 2. KPIs & Performance Measurement Define KPIs for AI adoption, utilisation, cost, and productivity. Establish baseline measurements and performance indicators. Distinguish productive AI usage from inefficient token consumption. Develop methods to measure improvements in development productivity, quality, and ROI. 3. AI-Assisted Software Development Optimisation Recommend effective AI practices across the software development lifecycle. Optimise context management, prompt engineering, model selection, and agentic workflows. Recommend suitable configurations and instructions for AI agents. Evaluate existing tools and identify alternative or complementary solutions. Balance productivity, quality, security, cost, and technical requirements. 4. Training & Competence Development Identify teams with low AI adoption and understand their challenges. Conduct practical workshops, coaching, and training for developers and technical leaders. Encourage knowledge sharing between experienced and less experienced AI users. Develop internal expertise and promote consistent AI-assisted engineering practices. 5. Governance & Continuous Improvement Establish methods for monitoring AI usage and performance. Recommend governance practices for AI-assisted development. Support experimentation, feedback, and continuous optimisation. Develop sustainable practices that adapt to evolving AI technologies and models. Expected Deliverables Current-state assessment of AI adoption and utilisation. Defined KPIs, measurement methods, and performance baselines. Evaluation of existing AI tools and alternative solutions. Recommendations for optimising AI-assisted software development. User-group segmentation based on AI adoption and usage patterns. Targeted improvement strategies for different user groups. Training materials, workshops, and coaching activities. AI governance and continuous monitoring recommendations. Final report with prioritised recommendations and implementation roadmap. Required Skills & Experience We are looking for an experienced AI specialist, consultant, or expert team with practical expertise in: Generative AI applications in software development. AI-assisted software engineering and developer productivity. GitHub Copilot and AI-powered development tools. Large Language Models (LLMs) and model selection. Prompt engineering and context management. AI agents and agentic development workflows. AI integration across the software development lifecycle. AI adoption metrics, usage analytics, and cost optimisation. Software engineering, code quality, testing, and security. AI governance and organisational transformation. Technical training, mentoring, and knowledge transfer. Experience supporting AI adoption across larger development organisations and working with safety-critical software environments is highly desirable. Engagement & Delivery Approach The organisation is open to different delivery models depending on the proposed solution and expected business benefits. Possible approaches include: AI strategy assessment and expert advisory. Hands-on technical implementation and optimisation. Workshops, coaching, and competence development. AI development frameworks, tools, and platform recommendations. Technical experiments and pilot projects. AI usage monitoring and continuous improvement. Access to a broader team of AI specialists and experts. Knowledge transfer is essential. The objective is to establish lasting internal capabilities rather than relying solely on individual expertise. Who Should Apply? We welcome applications from individual specialists and expert teams who combine hands-on Generative AI expertise with a strong understanding of software engineering. You should be capable of assessing AI maturity, identifying improvement opportunities, recommending suitable technologies, and helping development teams achieve measurable results from AI adoption. How to Apply Interested in helping engineering organisations unlock the full potential of Generative AI? Submit your profile or proposal through the Hykmann careers portal: 🌐 https://www.hykmann.com/careers Please highlight the following in your application: Experience with Generative AI and AI-assisted software development. Practical experience with GitHub Copilot and other AI tools. Knowledge of LLMs, prompt engineering, and agentic workflows. Examples of AI adoption and optimisation initiatives. Experience defining KPIs and measuring AI-driven business benefits. Experience delivering developer training and coaching. Your proposed assessment, optimisation, and knowledge-transfer approach. Availability of additional AI specialists or expert teams, where applicable.