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, competitive salary, and hands-on engineering experience.
- Other info: Opportunity for career growth in a dynamic tech environment.
- Why this job: Build impactful AI systems and collaborate with top engineering teams.
- Qualifications: 8-10 years of software engineering experience and AI system deployment.
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
AI Software Engineer employer: TekWissen UK
At TekWissen Group, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration. Our London-based AI Software Engineer role provides the opportunity to work hands-on with cutting-edge AI technologies in a hybrid environment, ensuring a perfect balance between professional growth and personal life. With a strong focus on employee development, we empower our team members to take ownership of impactful projects while enjoying the benefits of a diverse and inclusive workplace.
StudySmarter Expert Advice🤫
We think this is how you could land AI Software Engineer
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the AI and software engineering space. Attend meetups, webinars, or even just grab a coffee with someone in the industry. You never know who might have the inside scoop on job openings!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving AI systems and cloud-native development. This is your chance to demonstrate what you can do beyond your CV, so make it shine!
✨Tip Number 3
Prepare for technical interviews by brushing up on your coding skills and understanding of AI architectures. Practice common interview questions and maybe even do some mock interviews with friends or mentors to build your confidence.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got loads of opportunities waiting for talented individuals like you. Plus, it’s a great way to ensure your application gets seen by the right people.
We think you need these skills to ace AI Software Engineer
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. Keep it engaging and personal – we love to see your personality come through.
Showcase Your Projects:If you've worked on any AI-driven projects or have experience with agent-based architectures, make sure to showcase them in your application. We want to see real examples of your work and how you've tackled challenges in the past.
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 that 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 your knowledge of AI systems, especially agent-based architectures and RAG pipelines. Be ready to discuss your hands-on experience with AI/LLM-based 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 APIs, microservices, and tools like Docker and Kubernetes. Highlight specific projects where you’ve built scalable solutions and how you optimised performance.
✨Demonstrate Problem-Solving Abilities
Expect technical questions that test your problem-solving skills. Think of examples from your past work where you had to troubleshoot issues or optimise 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 projects and decisions in a way that’s easy to understand. This will show that you can collaborate effectively with both technical and non-technical teams.
