Principal AI/ML Engineer

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
• Frame business problems as ML problems, then design, train, and validate models (supervised learning, time series, forecasting, anomaly detection), including feature engineering and experimentation.
• 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.
• Analyze model performance and drive improvements in accuracy, robustness, and drift handling.
• Data privacy and synthetic data
• Apply data anonymization and tokenization so telecom data can be used for development, testing, and training while meeting customer privacy and regulatory requirements.
• Generate realistic synthetic data that reproduces the patterns and edge cases of production data, and validate its fidelity and usefulness.
• Define reusable tooling and standards for privacy-preserving data handling.
• ML engineering and production
• Design and evolve end-to-end training and inference pipelines for the core product ML solutions, from data processing through deployment.
• 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.
• Apply MLOps practices (monitoring, retraining, versioning, testing, deployment automation) that work across different customer environments.
• AI and agentic AI
• Use AI and agentic AI to improve development and operational processes (AI-assisted coding and review, testing, documentation, monitoring, data quality checks).
• Enhance traditional ML solutions with agentic capabilities where they add value, using shared platform components from the AI Innovation & Platforms team.
• Technical leadership
• Set technical standards for code and model review, testing, and reusable components, including guardrails for AI-assisted workflows.
• Mentor junior engineers and data scientists, and technically lead selected projects.
• Work with product, engineering, BU stakeholders, and telecom domain experts to keep delivery aligned with roadmap commitments.

Required Skills:

• [8+] years in applied ML, data science, or ML engineering, with a strong record of taking traditional ML solutions from problem framing to production.
• Proven experience building and validating ML solutions with scarce or no ground truth.
• Strong hands-on experience with data anonymization and tokenization, and with generating and validating realistic synthetic data.
• Strong expertise in machine learning (supervised learning, time series, anomaly detection, forecasting, predictive analytics).
• Working knowledge of deep learning, NLP, and generative AI/LLMs.
• Deep hands-on experience with PySpark/Spark, including performance tuning.
• Proven experience building ML training pipelines, production inference pipelines, and REST-based inference services.
• Strong Python and software engineering practices (testing, code review, CI/CD), and a solid Python ML stack (e.g., scikit-learn, PyTorch or TensorFlow).
• Experience with MLOps and model lifecycle management (e.g., MLflow or similar).
• Experience using AI-assisted development tools such as GitHub Copilot, ChatGPT, or similar platforms to improve engineering productivity.
• Experience mentoring others and acting as a technical reference.
• Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Statistics, or a related field, or equivalent experience.

Preferred Qualifications:

• Experience in the Telecom industry or with network and operations data.
• Experience as a vendor or solution provider delivering ML across multiple customer environments.
• Experience with differential privacy or other privacy-enhancing technologies, and with generative or simulation-based synthetic data.
• Hands-on experience integrating agentic AI or LLM-based solutions alongside traditional ML.
• Experience with cloud platforms (e.g., Azure, AWS, GCP).

Required Competencies:

• Technical Leadership & Mentoring
• Problem Framing & Ownership
• Analytical & Critical Thinking
• Communication of Technical Trade-offs
• Cross-functional Collaboration
• Pragmatic Decision-Making under Constraints
• Learning Agility & Innovation

Skills

ML, Machine Learning, PySpark, Python, Spark