At a Glance
- Tasks: Design and deploy AI-driven systems in complex enterprise environments.
- Company: Join a global leader in digital, cloud, and security services.
- Benefits: Hybrid work model, competitive salary, and hands-on engineering experience.
- Other info: Opportunity for career growth in a dynamic, innovative environment.
- Why this job: Build impactful AI systems and collaborate with top engineering teams.
- Qualifications: 8-10 years of software engineering experience and strong AI system knowledge.
The predicted salary is between 80000 - 100000 £ per year.
Role : AI Native Software Engineer
Location: London, UK
Work type: Hybrid
Duration: 6 Months
Job Summary:
We are seeking a hands‑on AI Native Software Engineer to design, build, and deploy production‑grade AI‑driven systems within complex enterprise environments.
In this role, you will focus on agent‑based architectures, AI platform integration, and cloud‑native development, delivering scalable, reliable solutions that power real business workflows.
This is a 100% hands‑on engineering role, ideal for a senior technologist who thrives at the intersection of AI systems, software engineering, and cloud infrastructure.
Key Responsibilities:
Core Duties
Design, implement, and maintain AI agent workflows, including retrieval‑augmented generation (RAG), orchestration, tool/function invocation, and policy‑based routing
Build cloud‑native backend services and APIs to support AI‑driven applications and enterprise integrations
Implement evaluation, monitoring, and observability frameworks to ensure accuracy, latency, reliability, and system health across AI agent lifecycles
Optimize AI and system performance across cost, scalability, and latency dimensions in production environments
Deliverables or Project Scope
Production‑ready AI‑powered applications aligned to defined business workflows and enterprise standards
Scalable multi‑model and multi‑provider AI architectures, including abstraction layers for provider flexibility
Fully deployed cloud‑native services using microservices, containers, and serverless or event‑driven patterns
Robust CI/CD pipelines, infrastructure‑as‑code implementations, logging, monitoring, and fault‑tolerant deployments
Collaboration Tools or Platforms
Microsoft Office (Excel, Word, Outlook, Teams)
AI Platforms & Models: OpenAI, Anthropic (Claude), Google Vertex AI, and select open‑source models
Agent & Orchestration Frameworks: LangGraph, AutoGen, CrewAI (or similar)
Cloud & DevOps Tooling: Docker, Kubernetes, Terraform, Helm, CI/CD pipelines
Enterprise Integration: APIs, enterprise platforms, monitoring and observability tools
Why You’ll Love This Role
Build real, enterprise‑grade AI systems that move beyond experimentation into production
Remain deeply technical in a 100% hands‑on engineering role with no people‑management responsibilities
Work with modern AI platforms, multi‑model architectures, and cloud‑native technologies
Focus on high‑impact delivery with clear scope, measurable outcomes, and implementation ownership
Collaborate with experienced engineering teams in an execution‑driven environment
Qualifications
~ Bachelor’s degree in Computer Science, Engineering, or a related technical field or equivalent practical experience
~8–10+ years of professional software engineering experience with ownership of production systems
~3+ years of hands‑on experience building and deploying AI/LLM‑based systems in production (agents, RAG pipelines, orchestration)
~ Strong experience designing and delivering cloud‑native systems, including APIs, microservices, containers, and serverless or event‑driven architectures
~ Proficiency in Python, Java, or comparable backend languages
~ Hands‑on experience with CI/CD pipelines, infrastructure as code, and monitoring or observability tools
~ Proven ability to deliver production‑quality code, including testing, debugging, performance tuning, and operational readiness
Preferred Qualifications
Experience with agent frameworks such as LangGraph, AutoGen, CrewAI, or similar
Experience designing multi‑agent or distributed AI systems
Familiarity with multi‑model and multi‑provider AI architectures
Experience integrating AI solutions into enterprise‑scale systems or platforms
Demonstrated experience optimizing AI workloads for cost, performance, and latency
Additional Information / Requirements
This is a 100% hands‑on engineering role with no people‑management responsibilities
Strong problem‑solving skills and technical judgment in complex enterprise environments
Ability to collaborate effectively with internal and client engineering teams
Comfortable working within existing architecture standards, security requirements, and engineering best practices
Strong written and verbal communication skills for technical documentation and design discussions
TekWissen® Group is an equal opportunity employer supporting workforce diversity
Experienced AI Software Engineer in London employer: TekWissen UK
TekWissen Group is an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration in the heart of London. With a strong focus on employee growth, you will have the opportunity to work on cutting-edge AI technologies in a hands-on role, while enjoying the benefits of a hybrid work environment and a commitment to diversity and inclusion.
StudySmarter Expert Advice🤫
We think this is how you could land Experienced AI Software Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the AI and software engineering space. Attend meetups, webinars, or tech conferences to meet potential employers and showcase your skills.
✨Tip Number 2
Show off your projects! Create a portfolio that highlights your hands-on experience with AI systems and cloud-native development. This is your chance to demonstrate your expertise beyond just a CV.
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and understanding of AI architectures. Practice common interview questions and be ready to discuss your past projects in detail.
✨Tip Number 4
Apply through our website! We make it easy for you to find roles that match your skills. Plus, it shows you're genuinely interested in joining our team at StudySmarter.
We think you need these skills to ace Experienced AI Software Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the AI Software Engineer role. Highlight your experience with AI systems, cloud-native development, and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about AI and how your background makes you a perfect fit for this role. Don't forget to mention specific technologies or frameworks you've worked with that relate to the job.
Showcase Your Projects:If you've got any personal or professional projects that demonstrate your skills in AI and software engineering, make sure to include them. We love seeing real-world applications of your work, especially if they involve cloud-native solutions or agent-based architectures!
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're serious about joining our team at StudySmarter!
How to prepare for a job interview at TekWissen UK
✨Know Your AI Stuff
Make sure you brush up on the latest AI technologies and frameworks mentioned in the job description, like LangGraph and OpenAI. Be ready to discuss your hands-on experience with AI systems and how you've tackled challenges in production environments.
✨Showcase Your Cloud Skills
Since this role involves cloud-native development, be prepared to talk about your experience with tools like Docker, Kubernetes, and Terraform. Have examples ready that demonstrate how you've built scalable and reliable cloud services in past projects.
✨Demonstrate Problem-Solving Abilities
Expect technical questions that assess your problem-solving skills. Think of specific scenarios where you've optimised AI workloads or improved system performance. Use the STAR method (Situation, Task, Action, Result) to structure your answers.
✨Communicate Clearly
Strong communication is key, especially when discussing complex technical concepts. Practice explaining your past projects and technical decisions in a way that's easy to understand. This will show your ability to collaborate effectively with teams and clients.
