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AI Roles Explained

Behind every breakthrough AI company is a team turning research into real products. While researchers and engineers build the models, the wider AI function ensures those models are trained responsibly, deployed safely, and reach the people and businesses who need them.

AI operations typically span three key areas: Applied Engineering, Trust & Safety/Legal, and Product. Together, they enable AI companies to scale responsibly, stay compliant, and build tools people actually trust and use.

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Areas of AI

Applied Engineering / MLOps

AI engineering is about turning research into reliable, production-ready systems. Unlike traditional software, AI systems depend on data quality, continuous retraining, and careful monitoring once they’re live.

Work in this area is iterative and infrastructure-heavy, focused on getting models from experiment to real-world scale.

  • Model training, fine-tuning and evaluation pipelines
  • Data pipeline management and dataset curation
  • Infrastructure for training and serving models at scale
  • Monitoring model performance, drift, and reliability in production

Trust, Safety & Legal

Trust and safety teams are essential in navigating the fast-evolving regulatory and ethical landscape surrounding AI. Their work ensures models are deployed responsibly while managing risk as laws and public expectations shift quickly.

Key areas of legal and safety operations include:

  • Compliance with AI regulation (EU AI Act, evolving global frameworks) and data protection law
  • Model risk assessment, red-teaming, and bias/safety evaluation)
  • Drafting IP, licensing and data-usage agreements for training data
  • Supporting responsible-AI governance and policy frameworks
  • Advising on copyright, liability, and cross-jurisdictional AI compliance

Legal teams provide the guardrails that allow innovation to scale safely.

Product

Product teams bridge the gap between research and real-world use, turning complex models into intuitive, user-centric products. They work closely with researchers, engineers, and designers to ensure every feature aligns with user needs, safety standards, and long-term goals.

Product responsibilities often include:

  • Defining AI product vision, strategy and roadmap
  • Translating user research and model capabilities into shippable features
  • Coordinating between researchers, engineers. designers, and stakeholders
  • Managing releases, evals, and quality throughout the product lifestyle
  • Ensuring AI products are safe, scalable, and explainable for end users

In AI, product teams blend strategy, UX, and technical fluency — shaping solutions that scale from early adopters to global users.

AI Careers

AI roles put you at the centre of one of the fastest-moving fields in technology. From applied engineering and MLOps to safety, compliance, and product delivery, your work directly shapes how AI systems are built and used.

If you thrive in fast-paced, high-stakes environments, enjoy solving novel problems, and want visible impact, a career in AI offers rapid growth and the chance to help shape how this technology reaches the world.

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Roles within AI

  • AI Engineer
  • Applied AI Engineer
  • Machine Learning Engineer
  • Research Engineer
  • Research Scientist
  • LLM Engineer
  • AI Agent Engineer
  • ML Infrastructure Engineer
  • AI Infrastructure Engineer
  • Forward Deployed AI Engineer
  • Applied Scientist
  • AI Platform Engineer
  • Inference Engineer
  • MLOps Engineer
  • GPU Infrastructure Engineer

How Odiin Supports AI Teams

Strong operations are critical to scaling AI companies. We support teams behind the scenes, ensuring organisations run smoothly, stay compliant, and scale responsibly.

We understand the unique operational challenges of AI companies — fast iteration cycles, evolving regulation, and globally distributed research teams — and connect companies with operators experienced in fast-moving, high-growth environments.

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