About Us:
Mobileum is a leading provider of Telecom analytics solutions for roaming, core network, security, risk management, domestic and international connectivity testing, and customer intelligence. More than 1,000 customers rely on its Active Intelligence platform, which provides advanced analytics solutions, allowing customers to connect deep network and operational intelligence with real-time actions that increase revenue, improve customer experience, and reduce costs.
Headquartered in Silicon Valley, Mobileum has global offices in Australia, Dubai, Germany, Greece, India, Portugal, Singapore and UK with global HC of 1800+.
Join Mobileum Team:
At Mobileum we recognize that our team is the main reason for our success. What does work with us mean? Opportunities!
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About the Role
The AI/ML Development team executes Business Unit product roadmaps, delivering traditional machine learning solutions and implementing agentic AI capabilities where the business and products need them.
We are a technology vendor serving the Telecom industry, and we are looking for a Principal AI/ML Engineer to be a senior technical owner of the ML solutions behind our products. The role is a hybrid that combines data science (developing and validating models) and ML engineering (building the training and inference pipelines and services that run them in production).
As a vendor, we often build models without unrestricted access to customer production data and with limited ground truth. We need someone who can deliver reliable solutions under those constraints, using privacy-preserving data practices and realistic synthetic data.
Roles & Responsibility:
Data science and model development
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Frame business problems as ML problems, then design, train, and validate models (supervised learning, time series, forecasting, anomaly detection), including feature engineering and experimentation.
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Build solutions where ground truth is scarce (weak/semi-supervised and unsupervised methods, transfer learning, domain rules, simulation, targeted expert labeling) and design evaluation strategies that stay credible with limited labels.
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Analyze model performance and drive improvements in accuracy, robustness, and drift handling.
Data privacy and synthetic data
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Apply data anonymization and tokenization so telecom data can be used for development, testing, and training while meeting customer privacy and regulatory requirements.
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Generate realistic synthetic data that reproduces the patterns and edge cases of production data, and validate its fidelity and usefulness.
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Define reusable tooling and standards for privacy-preserving data handling.
ML engineering and production
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Design and evolve end-to-end training and inference pipelines for the core product ML solutions, from data processing through deployment.
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Build production inference pipelines in PySpark and inference services with REST endpoints, and tune Spark jobs to cut processing time and infrastructure cost at telecom data volumes.
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Apply MLOps practices (monitoring, retraining, versioning, testing, deployment automation) that work across different customer environments.
AI and agentic AI
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Use AI and agentic AI to improve development and operational processes (AI-assisted coding and review, testing, documentation, monitoring, data quality checks).
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Enhance traditional ML solutions with agentic capabilities where they add value, using shared platform components from the AI Innovation & Platforms team.
Technical leadership
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Set technical standards for code and model review, testing, and reusable components, including guardrails for AI-assisted workflows.
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Mentor junior engineers and data scientists, and technically lead selected projects.
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Work with product, engineering, BU stakeholders, and telecom domain experts to keep delivery aligned with roadmap commitments.
Required Skills:
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[8+] years in applied ML, data science, or ML engineering, with a strong record of taking traditional ML solutions from problem framing to production.
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Proven experience building and validating ML solutions with scarce or no ground truth.
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Strong hands-on experience with data anonymization and tokenization, and with generating and validating realistic synthetic data.
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Strong expertise in machine learning (supervised learning, time series, anomaly detection, forecasting, predictive analytics).
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Working knowledge of deep learning, NLP, and generative AI/LLMs.
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Deep hands-on experience with PySpark/Spark, including performance tuning.
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Proven experience building ML training pipelines, production inference pipelines, and REST-based inference services.
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Strong Python and software engineering practices (testing, code review, CI/CD), and a solid Python ML stack (e.g., scikit-learn, PyTorch or TensorFlow).
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Experience with MLOps and model lifecycle management (e.g., MLflow or similar).
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Experience using AI-assisted development tools such as GitHub Copilot, ChatGPT, or similar platforms to improve engineering productivity.
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Experience mentoring others and acting as a technical reference.
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Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, Statistics, or a related field, or equivalent experience.
Preferred Qualifications
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Experience in the Telecom industry or with network and operations data.
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Experience as a vendor or solution provider delivering ML across multiple customer environments.
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Experience with differential privacy or other privacy-enhancing technologies, and with generative or simulation-based synthetic data.
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Hands-on experience integrating agentic AI or LLM-based solutions alongside traditional ML.
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Experience with cloud platforms (e.g., Azure, AWS, GCP).
Required Competencies
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Technical Leadership & Mentoring
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Problem Framing & Ownership
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Analytical & Critical Thinking
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Communication of Technical Trade-offs
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Cross-functional Collaboration
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Pragmatic Decision-Making under Constraints
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Learning Agility & Innovation
Work Experience: 8
Educational Qualification:
Location:
Know our story:
https://www.mobileum.com/
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LinkedIn: https://www.linkedin.com/company/mobileum/
Twitter: @MobileumInc
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