Hiring nowTelecommunications and network intelligence

Telecom AI Engineer

Also called Network AI Engineer

A Telecom AI Engineer builds AI services that help operators diagnose faults and run their networks. Done well, it means faster fault detection, steadier service quality and changes that do not break live traffic.

What they do

The work starts with network telemetry: alarms, counters, logs and performance data that are noisy and often incomplete. The engineer builds time-series models that spot faults and degradation, then wraps them in a service that operations teams can use. They connect models to operations systems, work with network engineers to understand what a real fault looks like, and test ideas in simulation before anything touches a live network. They also design how recommended changes are applied, checked and rolled back. Much of the day is spent on data quality, failure cases and the handover into incident response.

What a strong hire delivers

In the first months, a strong hire delivers a working network AI service that detects a defined set of faults from real telemetry. It comes with representative tests, including awkward cases such as missing data and cascading alarms. Operations staff trust and use it. Change recommendations are controlled, with clear limits and a way back. The team also gets a clear view of service quality before and after.

How you can tell they are good

Ask them to walk through one network or operations system they built, from raw telemetry to a live decision. Good candidates explain how they tested it, what went wrong and how they handled false alarms. Ask how they would stop an automated change from making an outage worse. Be wary of people who talk only about model accuracy and never about operators, rollback or incident response.

Why it is hard to fill

The mix is rare. Telecom engineers know networks but have often not shipped machine learning services. Data scientists can model well but have rarely lived with alarms, change windows and on-call. Job titles vary widely, and many good people are labelled as network engineers, operations engineers or analytics specialists. Candidates who combine both sides are few, and most are already trusted in their current teams.

Where to find them

Look in network operations and automation teams at operators, equipment makers and managed service providers. Strong candidates often come from telecom engineering, performance and assurance roles, or from data science teams that served a network business. Site reliability engineers and operations research specialists who have worked on large distributed systems can also move across well, if they are curious about the telecom domain.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Telecom AI Engineer

  1. Tell us about a model you built on network or similar operational telemetry. What did it detect and who used it?

    A strong answer names the data, the fault types and the real users. They describe messy inputs and how they dealt with them. Worry if the story ends at a notebook with no operational use.

  2. How would you reduce false alarms from a fault detection model without missing real incidents?

    Good answers discuss thresholds, alarm correlation, context from topology and feedback from operators. They accept a trade-off and say how they would measure it. Worry about a single accuracy number as the answer.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are building AI services that help people diagnose and operate communications networks. We are looking for an engineer who can take models from telemetry to a trusted service in live operations. You will work closely with network engineers and operations teams.

What you will do

  • Build and maintain AI services for network fault detection and diagnosis
  • Develop time-series models on network telemetry
  • Integrate models with operations systems and workflows
  • Create representative tests and simulations for network scenarios
  • Design controlled, reversible change behaviour for recommendations
  • Track fault detection and service quality and report on them
  • Work with operations teams during incidents and reviews

What you will bring

  • Shipped a model or analytics service used in network or similar operations
  • Practical experience with time-series modelling on noisy data
  • Sound grounding in distributed systems
  • Experience with reliability work such as incident response or on-call
  • Able to explain model behaviour to non-specialist operators
  • Evidence of testing systems where live experiments are limited

Nice to have

  • Experience with optimisation or operations research methods
  • Background in telecom engineering or network operations
  • Experience building simulations of network behaviour

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Time-series modellingAdvancedModel temporal dependencies and forecasts
  • Optimisation and operations researchAdvancedChoose actions subject to objectives and constraints
  • Reliability and incident responseWorkingDetect failures, restore service and prevent recurrence
  • Distributed systemsWorkingDesign reliable work across networked machines

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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