AI/ML ops

Job Description AI/ML ops

Department: Engineering

Level: Mid

Type: On-site

Location: Not specified

Work Mode: On-site

Experience: 7–10 years

Languages: ENglish (Professional), Arabic (Professional)

Building and operating systems that remain dependable after release.

About The Company Equal opportunity provider.

The work and what makes it matter

You will take ownership across production systems, deployment paths, data flows, observability, and incident response. In practice, the work moves from shipping model or data changes to instrumenting monitoring, investigating abnormal behaviour, improving recovery paths, and reducing repeat incidents. At this level, the expectation is independent execution, applied judgment, practical problem-solving, clear blocker communication, and reliable delivery of assigned workstreams. The work matters because reliability problems rarely arrive as one obvious failure; they surface through weak signals that need disciplined diagnosis and timely action.

What you'll bring

You bring 7–10 years of hands-on experience and a working command of Model Deployment, Continuous Integration/Continuous Deployment (CI/CD), Cloud Computing, Data Pipeline Management, Monitoring and Logging, Containerization, and Version Control. You can diagnose failures, make sensible trade-offs between reliability, latency, cost, and delivery speed, and explain decisions clearly. This calls for hands-on capability, role-relevant judgment, confidence with common situations, and ability to deliver without constant supervision. A bachelor's degree is the minimum education requirement. A master's degree is preferred, not required. The role requires ENglish (Professional) and Arabic (Professional). Because this role is on-site, willingness to relocate to Not specified is required.

What success looks like

By 90 days: understand the architecture, deployment path, data dependencies, and monitoring surface and deliver a well-scoped improvement with clear evidence

By 6 months: independently own a defined service, pipeline, model, or technical workstream, surface blockers early, and reduce recurring issues in the area

By year 1: be trusted to improve production reliability, engineering standards, and the team's ability to prevent recurring issues, make sound trade-offs, and strengthen how the team operates

What's Challenging About This Work Production systems often fail indirectly through drift, latency, stale data, capacity pressure, or incomplete signals. The difficult part is identifying which symptom points to the real cause before the issue becomes customer-facing. The person must know what to resolve directly, what needs deeper investigation, and when a wider decision is required.

How we hire & Equal Opportunity

This hiring process uses AI-assisted candidate assessments, including a TIPI personality assessment, a Voice communication assessment, and a Skills cognitive-capability assessment.

Brand Kiln Pvt. Ltd. is an equal opportunity employer and is committed to creating an inclusive environment for all applicants and employees. We do not discriminate on the basis of race, religion, colour, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.

About The Company Blockchain base AI

Industry

Technology

Company Size

1-10

Headquarters

India

Website

computeportal.io

Required Skills & Technologies Model DeploymentContinuous Integration/Continuous Deployment (CI/CD)Cloud ComputingData Pipeline ManagementMonitoring and LoggingContainerizationVersion Control

Job Code: CO-001-273AE8

Department: Engineering

Experience Level: Mid