Information technology, software development, data Jobs in Kenya

5 jobs found

Digital Divide Data

Manager, Operations (Machine Learning, Autonomous Vehicles & ADAS)

Nairobi

Kenya

Fortinet

Systems Engineer (Covering Tanzania)

Nairobi

Kenya

Palo Alto Networks

Manager, Technical Solutions

Nairobi

Kenya

All in One Software (E.A) Limited

.NET Software Developer

Nairobi

Kenya

Only on Fuzu
Code for Africa

AI Engineer

Nairobi

Kenya

Fortinet

Systems Engineer (Public Sector)

Nairobi

Kenya

Closed for applications
icolo.io

Engineering Intern

Nairobi

Kenya

Closed for applications
NTT Limited

End User Services – Digital Workplace Security Engineer

Nairobi

Kenya

Closed for applications

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CloudFactory

Senior Data Analyst

Nairobi

Kenya

Closed for applications
Digital Divide Data

Computers + 1 more

Manager, Operations (Machine Learning, Autonomous Vehicles & ADAS)

Job details

Contract Type

Description

Education & Experience

  • Bachelor’s degree in Data/AI, Computer Science, Engineering, Information Systems, or related fields.
  • A minimum of 2.5 years of experience in AI/ML operations, project management, or technical workflow coordination.
  • Hands-on exposure to annotation workflows: 2D/3D CV, LiDAR, ADAS, or AV datasets.
  • Strong track record managing projects in KPI-driven environments.
  • Must have worked in a BPO
  • Familiarity with annotation tools such as CVAT, SuperAnnotate, and Labelbox.
  • Understanding of ML metrics, data quality principles, and AV/ADAS ecosystems.


Responsibilities

Client Relationship & Communication Excellence

  • Build trusted relationships with ML, AV, and ADAS clients to ensure seamless service delivery.
  • Understand and articulate project scope, deliverables, timelines, and ownership.
  • Serve as the primary liaison for all client requests, updates, and issue resolution.
  • Track, manage, and close client requests with clarity, urgency, and professionalism.
  • Ensure workflows and outputs fully align with client expectations and technical guidelines.

Operational Delivery Ownership

  • Oversee day-to-day execution of annotation, QA, audits, and reporting activities.
  • Translate technical guidelines into clear, actionable workflows for delivery teams.
  • Monitor team adherence to SLAs/KPIs: accuracy, throughput, productivity, and latency.
  • Lead real-time issue resolution and ensure teams maintain context and operational readiness.
  • Maintain strict version control of instructions, guidelines, and workflow updates.

Performance Tracking & Continuous Improvement

  • Track performance trends across ML/AV/ADAS datasets using scorecards and dashboards.
  • Diagnose quality or productivity gaps and implement root-cause fixes.
  • Partner with QA and Training teams to refine workflows, conduct refreshers, and clarify instructions.
  • Lead performance reporting to clients, highlighting insights, actions, and operational improvements.

Team Leadership & Talent Development

  • Mentor and develop teams handling AI, CV, 3D, or LiDAR datasets.
  • Build a culture of feedback, technical excellence, and continuous learning.
  • Support team decision-making on ambiguous, complex, or escalated annotation scenarios.
  • Advise on capacity planning, calibration cycles, and training needs.

Risk Management & Issue Mitigation

  • Identify risks related to workflow complexity, guideline ambiguity, tooling inefficiencies, or data quality concerns.
  • Develop mitigation strategies to ensure delivery continuity and client satisfaction.
  • Support Business Continuity Plans (BCP) and drive readiness for activation.
  • Escalate advanced risks to senior leaders and product teams for resolution.


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