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- Tasks: Design and deploy cutting-edge agentic AI systems for real-world applications.
- Company: Join a fast-moving team at the forefront of applied AI.
- Benefits: Competitive salary, bonuses, remote work, and opportunities for international collaboration.
- Why this job: Make a real impact by delivering production-ready AI systems that change industries.
- Qualifications: Experience in building LLM/agentic systems and strong software engineering skills.
- Other info: Enjoy a dynamic culture with excellent growth opportunities and autonomy.
The predicted salary is between 54000 - 84000 ÂŁ per year.
🚀 Senior Agentic AI Engineer 🚀
Level: Associate Director
Location: Remote-first, with occasional travel for client delivery
Compensation: Competitive salary + bonus + benefits
Why This Role?
This is not “just another AI job.” We’re seeking engineers who can design and deploy agentic AI systems from the ground up — moving beyond demos into robust production systems. You’ll work directly with enterprise and public sector organisations, taking projects from PoC through to mission-critical deployments serving 100s–1000s of users.
What You’ll Do
- Design agentic systems – Own the agent loop (plan → act → reflect), orchestration (DAG/state machine), and memory management.
- Build robust tooling – Create schemas, retries, rate limits, and SDKs for safe extensibility.
- Engineer memory & knowledge services – Implement episodic/semantic memory, retrieval APIs, deduping, and summarisation agents.
- Productionise at scale – Deploy containerised systems (K8s, serverless, GPU pools), implement CI/CD, observability, and auto-scaling.
- Ensure robustness – Apply guardrails, cost/latency budgets, checkpointing, and critic/reviewer patterns.
- Evaluate rigorously – Build automated evaluation harnesses, use golden sets, retriever validation, and drift monitoring.
- Engage stakeholders – Run workshops, roadmap sessions, and maturity assessments, translating complex AI concepts into actionable strategies.
What We’re Looking For
Required:
- Proven track record building and deploying LLM/agentic systems into production.
- Strong software engineering foundations: orchestration, memory, deployment, monitoring.
- Familiarity with agentic architectures (e.g. LangGraph, ReAct, CoT loops) and/or ability to build without frameworks.
- Confident in client-facing settings: workshops, presentations, advisory roles.
Preferred:
- Advanced degree in AI, Computer Science, or related field.
- Experience with PyTorch/TensorFlow, vector databases, RAG, and orchestration tools.
- Background in start-ups (hands-on generalist) or consultancies (client-facing).
- Exposure to regulated industries.
- Independent, entrepreneurial mindset; comfortable working with autonomy.
Why Join?
- Impact: Deliver production-ready agentic AI systems in real-world environments.
- Innovation: Work on orchestration and deployment challenges that go beyond “glue code.”
- Reach: Remote-first role with opportunities for international collaboration.
- Growth: Influence the direction of enterprise AI adoption and contribute to the wider AI ecosystem.
- Culture: Join a fast-moving team operating at the frontier of applied AI.
Agentic AI, Multi-Agent Systems, LLM Deployment, LangGraph, LangChain, RAG, AI Orchestration, AI Consultant, Software Engineering for AI, AI Productionisation, Autonomous AI Systems, AI Architect, AI Careers, AI Jobs Remote.
Artificial Intelligence Engineer employer: Omnis Partners
Contact Detail:
Omnis Partners Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Artificial Intelligence Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI field on LinkedIn or at meetups. We can’t stress enough how important it is to make connections; 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 related to agentic AI systems. We love seeing real-world applications of your work, so make sure to highlight your best stuff!
✨Tip Number 3
Prepare for interviews by practising common questions and scenarios specific to AI engineering. We recommend doing mock interviews with friends or using online platforms to get comfortable discussing your experience and technical skills.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we’re always on the lookout for passionate candidates who want to make an impact in the AI space.
We think you need these skills to ace Artificial Intelligence Engineer
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the role. Highlight your experience with agentic AI systems and any relevant projects you've worked on. We want to see how your skills align with what we're looking for!
Showcase Your Technical Skills: Don’t hold back on detailing your technical expertise! Mention your experience with orchestration, memory management, and deployment tools. We love seeing candidates who can demonstrate their hands-on experience in building robust AI systems.
Be Client-Facing Ready: Since this role involves engaging with stakeholders, share examples of your client-facing experiences. Whether it’s running workshops or presenting complex ideas, we want to know how you communicate effectively in professional settings.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Omnis Partners
✨Know Your AI Stuff
Make sure you brush up on your knowledge of agentic AI systems and architectures. Be ready to discuss your experience with LLMs, memory management, and deployment strategies. The more specific examples you can provide from your past work, the better!
✨Showcase Your Problem-Solving Skills
Prepare to talk about how you've tackled complex challenges in previous roles. Think about times when you had to design robust tooling or implement CI/CD processes. Use the STAR method (Situation, Task, Action, Result) to structure your answers.
✨Engage with Stakeholders
Since this role involves client-facing interactions, practice how you would explain complex AI concepts to non-technical stakeholders. You might even want to prepare a mini-presentation or workshop outline to demonstrate your ability to translate technical jargon into actionable insights.
✨Be Ready for Technical Questions
Expect some deep dives into your technical expertise. Brush up on orchestration tools, memory services, and evaluation techniques. They might ask you to solve a problem on the spot, so be prepared to think critically and articulate your thought process clearly.