Senior Software Engineer

Microsoft Microsoft · Big Tech · London, United Kingdom +1 · Software Engineering

Senior Software Engineer role focused on designing and delivering AI/ML/LLM-based solutions for enterprise customers, involving prompt engineering, RAG, deployment, operation, evaluation, and monitoring of AI systems using cloud platforms.

What you'd actually do

  1. Build and ship solutions that meet enterprise security standards (threat modeling, secure coding, privacy, and compliance) from design through production.
  2. Translates business needs into technical solutions: Collaborates with appropriate stakeholders to determine user requirements for a scenario.
  3. Drives identification of dependencies and the development of design documents for a product, application, service, or platform
  4. Use modern engineering practices (CI/CD, automated testing, observability, and progressive delivery) to iterate fast and reduce operational risk.
  5. Work directly with customer engineering teams to deliver production-ready solutions, unblock delivery, and ensure outcomes are adopted at scale.

Skills

Required

  • Bachelor’s degree in computer science, or related technical discipline and technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • Experience building or integrating AI/ML or LLM-based solutions
  • prompt engineering
  • RAG
  • Familiarity with deploying and operating AI systems in production environments
  • Understanding of model evaluation, data quality, and performance monitoring
  • Experience using cloud AI platforms (Microsoft Foundry, OpenAI, or similar)

Nice to have

  • Master’s degree in computer science or related technical field AND technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • Comfortable with travel up to 25% (role dependent)

What the JD emphasized

  • Experience building or integrating AI/ML or LLM-based solutions
  • prompt engineering
  • RAG
  • deploying and operating AI systems in production environments
  • Understanding of model evaluation
  • data quality
  • performance monitoring

Other signals

  • building or integrating AI/ML or LLM-based solutions
  • prompt engineering
  • RAG
  • deploying and operating AI systems in production
  • model evaluation
  • data quality
  • performance monitoring
  • cloud AI platforms