Product Manager - AI

NetMind.AI
London
1 year ago
Applications closed

Related Jobs

View all jobs

Product Manager - Machine Learning

Product Manager - Machine Learning

AI Product Manager - Data Science (Energy) - London

AI Product Manager - Data Science (Energy) - London

Product Analytics Data Analyst - Funnel & Conversion

Machine Learning Engineer

At NetMind.ai, we’re building the next-generation AI/ML platform powered by a global decentralized GPU infrastructure. Our mission is to deliver the simplest and most accessible generative AI solutions on the market and democratize access to AI technology globally. Our AI services range from inference model APIs, training and fine-tuning, GPU clusters, agentic workflows, to AI consulting—empowering organizations of all sizes and AI developers to seamlessly adopt AI in diverse industries. If you’re passionate about building 0-to-1 AI products, thrive in fast-moving environments, and can bridge deep technical expertise with customer-driven innovation, join us as we shape the future of decentralized AI computing.


Responsibilities

  • You are the primary driver for identifying significant near and long-term opportunities in a large product area, and driving product vision, strategies and roadmaps, ensuring alignment with company goals and the rapidly evolving AI landscape.
  • Own the end-to-end customer experience for users building AI-powered applications and using AI services, proactively identifying and addressing customer pain points to increase adoption.
  • Work closely with cross-functional teams to drive product vision, define product requirements, coordinate resources from other groups (design, legal, etc.), and guide the team through key milestones.
  • Stay updated on the latest AI products, trends, technologies, and competitive landscape, and use this knowledge to inform product roadmaps and decision-making.
  • Conduct customer interviews, market research, and data analysis to define and validate product success metrics, while tracking adoption, retention, and performance to drive data-driven improvements and optimizations.
  • Develop strategies for product launches, customer onboarding, and marketing campaigns in collaboration with leadership, marketing, and business development teams.
  • Manage and build partnerships with AI model providers, computing resource providers, and other innovators in the GenAI ecosystem to enhance the platform.


Minimum Qualifications

  • 2+ years of product management or related industry experience.
  • Bachelor's degree in Computer Science, Engineering, Information Systems, Analytics, Mathematics, Physics, Applied Sciences, or a related field.
  • Skilled in full product lifecycle management, from ideation to launch, with experience integrating customer feedback into product requirements, driving prioritization, and managing pre/post-launch execution.
  • Good technical understanding of machine learning, large language models, model training, inference, and launching AI experiences.
  • Good understanding of cloud infrastructure, services, and architecture, with hands-on experience in cloud product development and deployment.
  • Experience working in a technical environment with a broad, cross-functional team to drive product vision, define product requirements, coordinate resources from other groups (design, marketing, etc.), and guide the team through key milestones.
  • Experience gathering requirements across diverse areas and users, and converting and developing them into a product solution.
  • Proven communication skills with experience delivering technical presentations.
  • Experience analyzing complex, large-scale data sets and making decisions based on data.


Preferred Qualifications

  • Proven experience leveraging ML/AI to build large-scale consumer products from 0 to 1.
  • Strong understanding of Generative AI technologies, including LLMs, RAG, agentic workflows, etc.
  • Master’s degree in AI/ML, Computer Science, or a related field.
  • Hands-on knowledge of MLOps workflows, model lifecycle management, and scalable inference.

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.

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

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

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

Machine learning has moved from academic research into the core of modern business. From recommendation engines and fraud detection to medical imaging, autonomous systems and language models, machine learning now underpins many of the UK’s most critical technologies. Universities have responded quickly. Machine learning modules are now standard in computer science degrees, specialist MSc programmes have proliferated, and online courses promise to fast-track careers in the field. And yet, despite this growth in education, UK employers consistently report the same problem: Many candidates with machine learning qualifications are not job-ready. Roles remain open for months. Interview processes filter out large numbers of applicants. Graduates with strong theoretical knowledge struggle when faced with practical tasks. The issue is not intelligence or effort. It is a persistent skills gap between university-level machine learning education and real-world machine learning jobs. This article explores that gap in depth: what universities teach well, what they routinely miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in machine learning.