Senior Generative AI Engineer

Insight Global
London
1 year ago
Applications closed

Related Jobs

View all jobs

Senior Lead Analyst - Data Science_ AI/ML & Gen AI

Machine Learning Engineer

Machine Learning Engineer (Manager)

Staff / VP, Data Scientist (UK)

Senior Machine Learning Engineer - LLM

Senior Machine Learning Engineer

Insight Global is looking for a motivated Generative AI Engineer to join one of our largest clients in the Pharmaceutical Manufacturing industry. This role is pivotal in optimizing and deploying AI/ML solutions that drive the future of drug development. If you’re excited about revolutionizing AI/ML in the pharmaceutical industry and collaborating with a diverse team of AI/ML scientists, scaling up exploratory work, and transitioning solutions from notebooks to robust ML pipelines, this could be your next career move.



Responsibilities:

  • Collaborative Innovation: working directly with AI/ML scientists on optimization and production deployment of solutions, creating blueprints, and acting as an internal consultant to transition ideas from prototype to production.
  • Taking Models into Production: Collaborate with data scientists to deploy machine learning models into production environments.
  • Data Exploration & Visualization: Exploring and visualizing data to understand those and identify differences in data distribution that could affect model performance when deployed in real-world scenarios.
  • Data Quality Assurance: Verifying data quality and ensuring it through data cleaning and ML validation strategies.
  • Building training pipelines and components to ensure scalable ML solutions, address errors, and provide education to upskill teams working on ML, enhancing MLOps proficiency.
  • Scaling up exploratory work, and transitioning solutions from notebooks to robust ML pipelines


Must Haves:

  • Experience as a Generative AI Engineer
  • Coming from an ML Engineering background
  • Comfortable building out ML infrastructure and deploying ML solutions
  • Strong experience building LLMs (large languages models) focusing on fine tuning, pretraining, inference, RAG (Retrieval augmented generation), and building multi-agent workflows
  • Using Llamaindex or Langchain
  • Experience working in NLP and very comfortable programming in Python and (TensorFlow, PyTorch, HuggingFace, etc) to be able to utilise and work on Deep Learning projects
  • Practical knowledge of data tools such as Kubernetes, Databricks or similar
  • Experience or understanding of building AI agents
  • Background working with technical data scientists, data engineers, and life scientists


Plusses:

  • Previous experience working in the Pharmaceutical industry
  • PhD focusing on Deep Learning, Neural Networks, Machine Learning or AI

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.