Software GenAI Engineer – Senior Consultant

Visa
Reading
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist Senior Consultant

Senior Machine Learning Engineer

Data Scientist / AI Engineer

Data Scientist

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

Senior Lead Analyst - Data Science - Machine Learning & Gen AI - UK

Job Description

This role requires an experienced GenAI engineer with a passion for working on LLM-based applications. The team is tasked with building key GenAI applications and scalable pipelines. The successful candidate should have a proven track record of developing multiple GenAI applications and be adept at managing the entire spectrum of GenAI application development while leading multiple workstreams.
Key responsibilities include:

  • Leading and delivering specific project deliverables as a Senior GenAI Engineer
  • Providing guidance to the engineering team on building new LLM applications and leveraging existing GenAI applications
  • Acting as the GenAI projects design authority
  • Shaping best practices and methodologies within the team

This role involves 70% GenAI and 30% Core Payments application development. The successful candidate should be open to working on payments application development to understand the current processes and suggest enhancements to improve productivity using LLM models. This position offers an excellent opportunity for a candidate with strong AI engineering credentials to increase their knowledge and experience in the payments industry.

This is a hybrid position. Hybrid employees can alternate time between both remote and office. Employees in hybrid roles are expected to work from the office 2-3 set days a week (determined by leadership/site), with a general guidepost of being in the office 50% or more of the time based on business needs.


Qualifications

Basic Qualifications

  • 8+ years of relevant work experience with a Bachelor’s Degree or at least 5 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 2 years of work experience with a PhD, OR 11+ years of relevant work experience.

Preferred Qualifications

  • 9 or more years of relevant work experience with a Bachelor Degree or 7 or more relevant years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 3 or more years of experience with a PhD
  • Bachelor’s Degree in Computer Science, Electronics/ Electrical Engineering or a related technical discipline is required
  • Strong knowledge of AI and data technologies, with expertise in Python
  • Experience in using machine learning technologies such as TensorFlow, PyTorch, and Scikit-learn is a plus.
  • Demonstrated experience in developing scalable AI pipelines and integrating models into real-time systems.
  • Proven track record of managing and executing multiple high-impact LLM based projects, balancing delivery speed with quality.
  • Able to showcase the productivity improvement by collecting relevant metrics.
  • Self-driven and act as a leader in the GenAI space within the department.
  • Act as a mentor to the rest of the developers to leverage existing and new GenAI toolsets.
  • Ability to communicate complex AI concepts to both technical and non-technical stakeholders.
  • Spearhead development, embedding, automation, and operation of scalable AI applications.
  • Ensure technical quality and reliability of AI solutions through rigorous testing, validation, and implementing frameworks for scalable data ingestion.
  • Optimize AI application performance and efficiency.
  • Collaborate across teams to drive AI innovation, while expanding your expertise within an experienced, inclusive, and international team.
  • Extensive relevant mid level work experience
  • Proficient in Python, software development, and application of AI models.
  • Highly skilled in realizing the full potential of LLM-based AI frameworks.
  • Electronic payment systems experience is preferred
  • Hands on experience on Golang is preferred
  • Ability to take ownership of open ended and highly complex problems and drive them to completion
  • Ability to work effectively on multiple concurrent assignments with both AI and non-AI applications projects
  • Excellent communication skills, with examples of influencing, listening actively and negotiating within a team environment to effectively advocate for Software Engineering best practice within the department and communicate design decisions effectively
  • Positive attitude, friendly to others, encouraging of co-operation, honesty, and respectfulness in the workplace
  • Collaborative mindset, with an ability to empathise with colleagues and establish relationships
  • Flexibility, self-motivated, high work standards, attention to detail, ability to perform as a leader of a team
  • Willingness and desire to learn new skills and take on new tasks and initiatives.
  • Proven ability to multi-task independently in a fast-paced environment and handle multiple competing priorities with minimal direction from management.
  • Should be process oriented and possess good planning and organizational skills



Additional Information

Visa is an EEO Employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with EEOC guidelines and applicable local law.

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.