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For Companies At a Glance
- Tasks: Design and implement cutting-edge AI systems that optimise real business operations.
- Company: Join a pioneering tech firm focused on mission-critical AI solutions.
- Benefits: Flexible hours, competitive pay, and the chance to work directly with founders.
- Why this job: Shape the future of AI while making a tangible impact in various industries.
- Qualifications: 3+ years in LLM systems, strong Python skills, and a passion for real-world applications.
- Other info: Contract role with potential for growth in a dynamic environment.
The predicted salary is between 48000 - 72000 £ per year.
We are building a mission-critical AI platform that automates and optimizes real business operations across industries and SMBs. This is not a chatbot, prototype, or experimentation environment. This is a safety-sensitive, trust-dependent production system where reliability, accuracy, and controlled behaviour matter.
We are looking for a senior engineer who can design and own the intelligence layer end-to-end from reasoning architecture to deployment economics. You will work directly with the founder to shape both the technical foundation and product behaviour. This role is for builders who have shipped real systems used by real users — and understand the responsibility that comes with that.
What You’ll Own- LLM architecture and orchestration strategy
- Conversational reasoning flows with deterministic control
- Long-term memory systems (consent-aware, structured, and verifiable)
- Summarization pipelines with traceability and drift monitoring
- Latency, cost, and reliability optimization at scale
- Production evaluation, guardrails, and failure handling
- AI behaviour alignment with product UX and risk boundaries
- Design and implement production-grade LLM workflows
- Architect memory that is accurate, bounded, and auditable
- Engineer systems that minimize hallucination and maximize trust
- Build observability into model reasoning and outputs
- Optimize token usage, routing strategies, and compute cost
- Define evaluation frameworks and continuous improvement loops
- Collaborate on product decisions that affect model behaviour
- Ship stable, maintainable, scalable infrastructure
- 3+ years building and deploying LLM systems in production
- Proven experience shipping real user-facing AI products
- Strong prompt architecture and system design capability
- Deep understanding of model limitations and failure modes
- Experience with memory design, retrieval, and summarization pipelines
- Ability to reason about tradeoffs: cost vs latency vs reliability vs safety
- Designing AI systems that support complex business workflows
- Multi-model routing and orchestration strategies
- Evaluation frameworks and behavioural testing
- Structured knowledge storage and retrieval architectures
- Reliability engineering for AI systems
- Working closely with product leadership in early-stage environments
- You build demos but haven’t shipped production systems
- You rely on generic prompt patterns without architecture thinking
- You cannot explain technical tradeoffs clearly
- You treat LLMs as black boxes
- You are uncomfortable with accountability for real-world impact
You design systems that behave predictably. You reduce uncertainty, not increase it. You build intelligence that organizations can rely on not just interact with. If that excites you, apply with detailed, technical answers.
Contract duration of 1 to 3 months, with 30 hours per week.
Mandatory skills: Python, Machine Learning, Artificial Intelligence, API, Open AI, Amazon Web Services, TensorFlow.
Senior AI Systems Engineer employer: FreelanceJobs
Contact Detail:
FreelanceJobs Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior AI Systems Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the AI and tech space, especially those who work at companies you're interested in. A friendly chat can open doors that a CV just can't.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repo showcasing your projects, especially those related to LLM systems. This gives potential employers a taste of what you can do beyond the application.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. Be ready to discuss your past projects in detail, especially how you've tackled challenges in production environments.
✨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 love seeing candidates who are proactive about their job search.
We think you need these skills to ace Senior AI Systems Engineer
Some tips for your application 🫡
Show Your Experience: Make sure to highlight your experience with building and deploying LLM systems in production. We want to see real examples of how you've shipped user-facing AI products, so don’t hold back on the details!
Be Technical and Specific: When answering questions, dive into the technical aspects. We’re looking for clarity on your thought process regarding prompt architecture, system design, and trade-offs. The more specific you are, the better we can understand your expertise.
Demonstrate Accountability: We value accountability, especially when it comes to real-world impact. Share instances where you’ve taken responsibility for the systems you’ve built and how you’ve ensured their reliability and accuracy.
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensure it gets the attention it deserves. We can’t wait to see what you bring to the table!
How to prepare for a job interview at FreelanceJobs
✨Know Your AI Systems Inside Out
Make sure you have a solid grasp of the LLM systems you've worked on. Be ready to discuss specific projects where you've designed and deployed production-grade AI solutions. Highlight your understanding of model limitations and how you've tackled them in real-world applications.
✨Showcase Your Problem-Solving Skills
Prepare to explain how you've approached complex trade-offs in your previous roles, such as balancing cost, latency, and reliability. Use examples to illustrate your thought process and decision-making when faced with challenges in AI system design.
✨Demonstrate Your Collaboration Experience
Since you'll be working closely with the founder and product leadership, share instances where you've collaborated effectively in early-stage environments. Discuss how your input shaped product decisions and improved model behaviour, showcasing your ability to work as part of a team.
✨Be Ready for Technical Deep Dives
Expect to dive deep into technical discussions about memory design, summarization pipelines, and observability in AI systems. Brush up on your knowledge of Python, TensorFlow, and AWS, and be prepared to answer questions that test your expertise in these areas.
