AI Solutions Architect (R122902 AI Solutions Architect) (Hiring Immediately)

Mars IS US
London
2 months ago
Applications closed

Related Jobs

View all jobs

Data & AI Solution Architect, Azure, Remote

AI) Machine Learning Research Engineer

Director of Generative AI | Remote

Data Science Manager – Gen/AI & ML Projects - Bristol

Machine Learning and AI Engineering Lead

Machine Learning Engineer, Amazon Studios AI Lab

AI Solutions Architect (R122902 AI Solutions Architect) (Hiring Immediately)

As anAI Solutions Architectat Mars Global Services, you will lead the design, integration, and deployment of AI-powered solutions to enhance the Associate experience, with a strong focus on Generative AI (GenAI) and Conversational AI. In this key role, you will drive AI transformation initiatives within a globally recognized brand, influencing enterprise-wide adoption of AI solutions. Your work will be pivotal in ensuring the successful implementation of scalable and secure AI solutions across Mars' enterprise platforms, while driving AI adoption across the organization.

What are we looking for?

  • Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, or a related field (or equivalent industry experience).
  • 7+ years of experience in AI/ML solution development, architecture, and enterprise integration.
  • Expertise in LLMs, NLP/NLU, Conversational/GenAI, AI Search, and Virtual Agents.
  • Proficiency in programming & AI development (Python, OpenAI APIs, MLOps frameworks).
    • Nice-to-Haves:
      • Experience with multilingual AI models for global translation.
      • AI certifications (e.g., Azure AI Engineer, Google ML Engineer, TOGAF).

What would be your key responsibilities?

  • Design and implement enterprise-scale AI solutions, focusing on Conversational AI, Generative AI, and AI-powered automation to enhance business operations.
  • Define and maintain the technical product roadmap, ensuring scalability, security, compliance, and alignment with business goals.
  • Develop and deploy custom AI models (NLU, NLG, AI Search, Virtual Agents) and integrate with SaaS platforms (e.g., ServiceNow, Workday, OpenAI) to improve user experience.
  • Establish AI governance frameworks to align with Responsible AI practices and ensure compliance with data privacy laws (e.g., GDPR, CCPA).
  • Drive adoption of GenAI-powered tools for self-service automation, analytics, and search capabilities, while providing leadership and mentorship to AI and engineering teams.
  • Identify and mitigate AI risks (e.g., model drift, data bias) and continuously refine AI models and solutions through performance monitoring and feedback loops.
  • Expertise in AI/ML algorithms, enterprise-scale applications, and SaaS AI platforms (e.g., ServiceNow Now Assist, Workday Illuminate, SAP, Microsoft CoPilot, OpenAI, Mistral), with experience integrating AI solutions with enterprise systems (Microsoft, Workday, SAP) to enable connected experiences across search and conversational AI.

What can you expect from Mars?

  • Work with over 130,000 diverse and talented Associates, all guided by the Five Principles.
  • Join a purpose driven company, where we’re striving to build the world we want tomorrow, today.
  • Best-in-class learning and development support from day one, including access to our in-house Mars University.
  • An industry competitive salary and benefits package, including company bonus.

#J-18808-Ljbffr

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 Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

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

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.