Senior Generative AI Engineer

KPMG
Plymouth
1 month ago
Applications closed

Related Jobs

View all jobs

Machine Learning Research Engineer | Generative Models | Protein Design | Deep Learning | Pytho[...]

Machine Learning and AI Engineering Lead

Machine Learning Engineer, London

Senior Machine Learning Engineer - Generative AI London

Senior Machine Learning Engineer (UAE Based)

Consultant/Senior Consultant - Data Science Customer Data & Technology

Job description

Senior Generative AI Engineer (C grade)
There has never been a been a better time to join the Data & AI team at KPMG. Our clients and communities we act in embrace the opportunities provided by AI, and are looking for help deploying GenAI in a fair, ethical and impactful way. The KPMG Data & AI team helps clients on theirAI transformation journeysby leveraging advanced analytical techniques and industrial-scale AI platforms. Our projects span industries such as Financial Services, Retail, Public Sector, Healthcare, Energy, and Utilities, with a focus on extracting data insights, building AI models, and delivering value through engaging, data-driven stories. Our approach is multi-disciplinary, so we are able to answer our clients’ most complex issues and have significant impact on their business results.

 

Role Overview:
KPMG UK is seeking aSeniorGenAI Engineerto join ourData & AI team. In this role, you will contribute to thedevelopmentanddeploymentofgenerative AI models, support client project delivery, and work collaboratively to drive impactful AI solutions. You will play an integral role in designing AI systems, managing teams, and ensuring that AI models are effectively integrated into client environments, all while adhering to data governance and security standards.

 

Key Responsibilities:

 

AI Solution Development:Contribute to the design, development, and implementation of generative AI models to address client business challenges.Collaborate with senior AI engineers and data scientists to build AI solutions that align with business goals.Participate in the creation of Proof of Concepts (PoCs), Minimal Viable Products (MVPs), and fully developed AI projects that drive business impact.Client Project Delivery & Team Management:Support the delivery of AI solutions for client projects, ensuring successful outcomes and timely execution.Manage and mentor a team of AI engineers and data scientists, providing guidance and support throughout the project lifecycle.Collaborate with cross-functional teams to gather client requirements, translate them into technical solutions, and ensure seamless implementation of AI models.Coding & Implementation:Develop and optimize generative AI models, ensuring high-quality code that meets production standards.Work with tools like TensorFlow, PyTorch, Databricks, and Snowflake to implement and deploy AI models in cloud environments.Ensure the integration of AI models into existing systems, managing version control and collaborating on continuous integration/continuous delivery (CI/CD) processes.Data Management & Integration:Work closely with data engineering teams to ensure smooth data flow, integration, and management for AI model development.Ensure AI models are well-integrated into existing data pipelines, with an emphasis on data quality and consistency.Adhere to best practices for data governance, security, and privacy, particularly in relation to sensitive client data.Business Development & Practice Building:Assist in identifying opportunities for AI solutions that meet client needs, supporting feasibility studies and the development of tailored data strategies.Contribute to business development efforts by supporting RFP responses, proposals, and client demos, highlighting the value of AI-driven solutions.Help expand KPMG's AI practice by bringing innovative ideas and solutions to clients and assisting in the growth of AI capabilities.Ethical and Secure AI deploymentEnsure AI models and data processing are compliant with KPMG’s data governance policies and industry regulations.Implement best practices in data privacy, security, and ethical AI, particularly when working with sensitive or regulated data.Contribute to the development of guidelines and frameworks for the secure handling of data in AI projects.

Qualifications & Experience:

 

Educational Background:We are keen to hear from people with the right skills and mindset. We think that this means you will likely have a degree in a related field (such as Computer Science, Statistics or a related field) – but that is not a must. If you have a degree in a different field, or no degree at all but significant professional experience in a related field, please consider applyingAdvanced certifications in AI/ML or course work are a bonus.

(We want to continue to build out our team with the best and brightest minds in the industry, and if you feel you can contribute to our strategic goals and our clients, we would love to hear from you)

Work Experience:5+ years of experience in AI/ML, with a focus on developing and deploying generative AI solutions.Proven experience working with Large Language Models (LLMs) like GPT, BERT, or similar technologies.Strong expertise in AI frameworks such as TensorFlow, PyTorch, and cloud platforms like Databricks and Snowflake.

 

 

Skills:Proficient in designing and coding generative AI models, including prompt engineering for LLMs like GPT and BERT.Strong understanding of AI/ML algorithms, model optimization, and deployment in scalable cloud environments.Experience with version control (e.g., Git), Docker, and data engineering tools such as Hadoop, Spark, and Elasticsearch.Excellent team collaboration, leadership, and communication skills, with the ability to manage and mentor junior team members.

 

Why KPMG?

Work with the most exciting clients: We help organisations across industries, from Financial Services, to Retailers, Public Sector and third sector. Both in the UK, and globally. Work on the most exciting projects: We help our clients solve their biggest problems. We spend time getting to know their organisations and we work in multi-disciplinary team developing complete solutions that drive impact. Spend time with brilliant, collaborative colleagues: We are often described as one of the most collaborative team clients (and colleagues) come across. Working for KPMG means that you will work alongside some of the most brilliant, and collegiate minds in the industry. Be part of a world leading innovator: KPMG Data & Technology regularly features as a leader or winner in the most prestigious analyst league tables. Get involved in some of the most innovative projects delivered collaboratively with our clients. Take charge of your career: With world leading training and development programmes, a culture of exploring your personal interest and opportunities across sectors, functions and areas of expertise, you will have ample opportunity to shape your career with KPMG. Feel a sense of achievement: Our approach to working with clients means that we make a real difference.  

Get the latest insights and jobs direct. Sign up for our newsletter.

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

Industry Insights

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

Portfolio Projects That Get You Hired for AI Jobs (With Real GitHub Examples)

In the fast-evolving world of artificial intelligence (AI), an impressive portfolio of projects can act as your passport to landing a sought-after role. Even if you’ve aced interviews in the past, employers in AI and machine learning (ML) are increasingly asking candidates to demonstrate hands-on experience through the projects they’ve built and shared online. This is because practical ability often speaks volumes about your suitability for a role—far more than any exam or certification alone could. In this article, we’ll explore how to build an outstanding AI portfolio that catches the eye of recruiters and hiring managers, including: Why an AI portfolio is crucial for job seekers. How to choose AI projects that align with your target roles. Specific project ideas and real GitHub examples to help you stand out. Best practices for showcasing your work, from writing clear READMEs to using Jupyter notebooks effectively. Tips on structuring your GitHub so that employers can instantly see your value. Moreover, we’ll discuss how you can use your portfolio to connect with top employers in AI, with a handy link to our CV-upload page on Artificial Intelligence Jobs for when you’re ready to apply. By the end, you’ll have a clear roadmap to building a portfolio that will help secure interviews—and the AI job—of your dreams.

AI Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

In today's competitive AI job market, nailing a technical interview can be the difference between landing your dream role and getting lost in the crowd. Whether you're looking to break into machine learning, deep learning, NLP (Natural Language Processing), or data science, your problem-solving skills and system design expertise are certain to be put to the test. AI‑related job interviews typically involve a range of coding challenges, algorithmic puzzles, and system design questions. You’ll often be asked to delve into the principles of machine learning pipelines, discuss how to optimise large-scale systems, and demonstrate your coding proficiency in languages like Python, C++, or Java. Adequate preparation not only boosts your confidence but also reduces the likelihood of fumbling through unfamiliar territory. If you’re actively seeking positions at major tech companies or innovative AI start-ups, then check out www.artificialintelligencejobs.co.uk for some of the latest vacancies in the UK. Meanwhile, this blog post will guide you through 30 real coding & system-design questions you’re likely to encounter during your AI job interview. This list is designed to help you practise, anticipate typical question patterns, and stay ahead of the competition. By reading through each question and thinking about the possible approaches, you’ll sharpen your problem-solving skills, time management, and critical thinking. Each question covers fundamental concepts that employers regularly test, ensuring you’re well-equipped for success. Let’s dive right in.

Negotiating Your AI Job Offer: Equity, Bonuses & Perks Explained

Artificial intelligence (AI) has proven itself to be one of the most transformative forces in today’s business world. From smart chatbots in customer service to predictive analytics in finance, AI technologies are reshaping how organisations operate and innovate. As the demand for AI professionals grows, so does the complexity of compensation packages. If you’re a mid‑senior AI professional, you’ve likely seen job offers that include far more than just a base salary—think equity, bonuses, and a range of perks designed to entice you into joining or staying with a company. For many, the focus remains squarely on salary. While that’s understandable—after all, your monthly take‑home pay is what covers day-to-day expenses—limiting your negotiations to salary alone can leave considerable value on the table. From stock options in ambitious startups to sign‑on bonuses that ‘buy you out’ of your current contract, modern AI job offers often include elements that can significantly boost your long-term wealth and job satisfaction. This article aims to shed light on the full scope of AI compensation—specifically focusing on how equity, bonuses, and perks can enhance (or sometimes detract from) the overall value of your package. We’ll delve into how these elements work in practice, what to watch out for, and how to navigate the negotiation process effectively. Our goal is to provide mid‑senior AI professionals with the insights and tools to land a holistic compensation deal that accurately reflects their technical expertise, leadership potential, and strategic importance in this fast-moving field. Whether you’re eyeing a leadership role in machine learning at an established tech giant, or you’re considering a pioneering position at a disruptive AI startup, the knowledge in this guide will help you weigh the merits of base salary alongside the potential riches—and risks—of equity, bonuses, and other benefits. By the end, you’ll have a clearer sense of how to align your compensation with both your immediate lifestyle needs and long-term career aspirations.