Roles

Routes into AI engineering

Every roadmap is built around one of these. Pick the closest fit when you sign up — you can change it at any point, and the roadmap rebuilds around the new target.

AI engineering roles

  • AI Engineer

    Build and ship AI features: models, LLM apps, RAG and evals

    Builds AI into products end to end, from training or fine-tuning models to LLM apps with retrieval, agents and evals.

    Typical background: Python SWEs, data scientists or ML engineers moving into production AI.

  • AI Platform / MLOps

    Run the infrastructure that serves AI models

    Runs the infrastructure that trains, deploys and monitors models reliably.

    Typical background: DevOps, SRE, cloud or data engineers.

  • AI Backend Engineer

    Backend services that integrate LLMs and models

    Integrates AI into product backends: APIs, LLM calls, retrieval and reliability.

    Typical background: Java/Spring or Python microservices + LLM integration.

  • AI Architect / Lead

    Design AI platforms and lead teams (10+ years)

    Designs end-to-end AI systems and guides teams on cost, risk and strategy.

    Typical background: Senior MLEs/architects with platform and governance experience.

More roles

  • Data Engineer

    Build the data pipelines behind AI

    Builds the pipelines and datasets that models learn from and serve on.

    Typical background: ETL, BI, big-data engineers (common from TCS/Infosys).

  • Cloud Engineer — AWS / GCP / Azure

    Build and run the cloud platforms AI workloads depend on

    Designs, automates and secures cloud infrastructure on AWS, GCP or Azure: networking, compute, storage and cost.

    Typical background: Sysadmins, DevOps or support engineers with cloud certifications.