Level

Version 1

AI Engineer

Version 1 is hiring an AI Engineer in London, United Kingdom. Level rates it ; you can apply on Level.

AI in this role

langchainllamaindexlanggraphsemantic-kernelhugging-facepytorchtensorflowscikit-learnmlflowdatabricks
ragllm-integrationai-agentsml-opsai-evaluation

Version 1 has celebrated 30 years in business and continues to be trusted by global brands to deliver technology and transformation solutions that drive customer success. Our deep expertise enables our customers to navigate the rapidly evolving technology landscape. We foster strong partnerships with global technology leaders including Microsoft, AWS, Oracle, Red Hat, OutSystems, Snowflake, ensuring that our customers are provided with the highest quality solutions and services.

 We’re an award-winning employer reflecting how our employees are at the very heart of  what we do:

  • UK & Ireland's premier AWS, Microsoft & Oracle partner
  • 3300+ strong, €350/£300m revenue business
  • 10+ years as a Great Place to Work in Ireland & UK
  • Best Workplace for Women in the UK & Ireland by GPTW
  • Best Workplace for Wellbeing in the UK by GPTW

We’re a core valuesdriven company, we hire people who share our values, and we reward those who display and foster them, it’s deeply embedded within our DNA. Invest in us and we’ll invest in you.

We are seeking a hands-on AI/ML Engineer to build, deploy, and optimize machine learning and Generative AI solutions. The role focuses on developing production-grade AI applications, integrating AI models into business systems, and supporting the full AI development lifecycle.

The ideal candidate is passionate about modern AI technologies, software engineering best practices, and delivering reliable AI solutions at scale.

Key Responsibilities

AI Application Development

  • Design, build, and maintain AI-powered applications and services.
  • Develop and deploy Machine Learning and Generative AI solutions.
  • Build Retrieval-Augmented Generation (RAG) systems and AI agents.
  • Integrate foundation models and APIs into enterprise applications.
  • Create reusable AI components and frameworks.

Machine Learning Engineering

  • Train, fine-tune, evaluate, and deploy ML models.
  • Develop feature engineering and model evaluation pipelines.
  • Implement model monitoring and performance tracking.
  • Optimize model inference performance and cost efficiency.

Generative AI Engineering

  • Develop LLM-based applications and workflows.
  • Build prompt templates, agent frameworks, and orchestration pipelines.
  • Implement vector search and knowledge retrieval systems.
  • Design evaluation frameworks for AI quality, safety, and reliability.
  • Improve hallucination mitigation and response accuracy.

MLOps & Deployment

  • Design and automate ML model training, validation, and retraining pipelines.
  • Version and manage datasets, features, and model artefacts using tools such as MLflow or similar.
  • Deploy and serve ML models via REST APIs or batch inference at scale on Databricks, Snowflake or similar.
  • Monitor model performance in production, detecting drift and degradation.
  • Manage feature stores and ensure data pipeline reliability for ML workloads.
  • Implement experiment tracking and reproducibility across the model lifecycle.

Collaboration

  • Work closely with Solution Architects, Product Managers, and Engineering Teams.
  • Participate in code reviews and technical design discussions.
  • Support testing, deployment, and ongoing optimisation activities.

Programming & Engineering

  • Strong proficiency in:
    • Python
    • SQL

AI & Machine Learning

  • Experience with:
    • Scikit-learn
    • PyTorch and/or TensorFlow
    • Hugging Face
    • Main AI services from leading cloud service and model providers
    • Supervised and unsupervised learning
    • Model evaluation
    • Feature engineering
    • Statistical concepts

Generative AI

  • Practical experience building:
    • RAG systems
    • AI agents
    • LLM-powered applications
    • Vector search solutions
  • Familiarity with:
    • LangChain
    • LlamaIndex
    • Semantic Kernel
    • LangGraph

Cloud & Infrastructure

  • Experience with AWS.
  • Docker and containerization.
  • Kubernetes (preferred).
  • CI/CD pipelines and DevOps practices.

Professional Experience

  • 3+ years in ML engineering or data science, with demonstrable experience deploying models to production and building Gen AI applications including RAG systems, LLM integration, or agentic workflows
  • Demonstrated experience delivering AI solutions into production environments.

Why Version 1?

 At Version 1, we believe in providing our employees with a comprehensive benefits package that prioritises their wellbeing, professional growth, and financial stability.

  • Share in our success with our Quarterly Performance-Related Profit Share Scheme, where employees collectively benefit from a share of our company's profits
  • Strong Career Progression & mentorship coaching through our Strength in Balance & Leadership schemes with a dedicated quarterly Pathways Career Development programme
  • Flexible/remote working, Version 1 is tremendously understanding of life events and people’s individual circumstances and offer flexibility to help achieve a healthy work life balance
  • Financial Wellbeing initiatives including; Pension, Private Healthcare Cover, Life Assurance, Financial advice and an Employee Discount scheme
  • Employee Wellbeing schemes including Gym Discounts, Bike to Work, Fitness classes, Mindfulness Workshops, Employee Assistance Programme and much more. Generous holiday allowance, enhanced maternity/paternity leave, marriage/civil partnership leave and special leave policies
  • Educational assistance, incentivised certifications, and accreditations, including AWS, Microsoft, Oracle, and Red Hat
  • Reward schemes including Version 1’s Annual Excellence Awards & ‘Call-Out’ platform.
  • Environment, Social and Community First initiatives allow you to get involved in local fundraising and development opportunities as part of fostering our diversity, inclusion and belonging schemes.

And many more exciting benefits… drop us a note to find out more.

 

Version 1 is an equal opportunities employer.
 
We are committed to building a diverse, inclusive and respectful workplace where everyone feels valued and able to thrive. We welcome applications from people of all backgrounds, identities and lived experiences, and we value the different perspectives people bring.
 
We want every candidate to have a positive and accessible recruitment experience. If you need reasonable adjustments at any stage of the process, please contact varun.gill@version1.com at Version 1. We will consider all requests carefully, respectfully and confidentially.

Video links:

https://www.youtube.com/watch?v=F_d3ELTH5zo

#LI-SS1

How we rate this

AI Engineer at Version 1 rates 98 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

RAGLLM IntegrationAI agentsML OpsAI EvaluationLangChainLlamaIndexLangGraph

Questions you could be asked

  1. How would you design a retrieval step so the model answers from real data instead of guessing?
  2. How have you integrated a large language model into a production application?
  3. How do you decide when an AI agent can act on its own versus asking for approval first?
  4. How do you monitor a model once it's live, and how do you know it needs retraining?
  5. How do you decide that one model's output is better than another's for a given task?

Adapt your resume

  • List these exact terms on your resume: RAG, LLM Integration, AI agents, ML Ops, and AI Evaluation. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
  • Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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