Lead Data Science & ML Ops - ML migration

Overview:

Job Summary: -

Design and oversee the architecture of ML, data science, and MLOps systems tailored for large-scale telecom environments. Ensure scalability, robustness, and efficient lifecycle management of models addressing telecom-specific challenges.

Responsibilities:

Key Responsibilities: -

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  Architect end-to-end ML and DS solutions incorporating telecom domain knowledge (wireline, wireless, NQES, churn, SINR, Video on Demand, FWA).
• Define and implement MLOps strategies for continuous integration, deployment, monitoring, and governance of telecom ML models.
• Collaborate with data scientists, engineers, and DevOps teams to streamline workflows and infrastructure for telecom data pipelines and models.
• Evaluate and recommend tools, frameworks, and platforms optimized for telecom ML and DS projects.
• Ensure security, compliance, scalability, and reliability of telecom ML systems.
• Provide technical leadership and mentorship in both architecture and telecom domain best practices.

Requirements:

Skills and Requirements: -

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  Deep expertise in ML frameworks (TensorFlow, PyTorch), MLOps tools (Kubeflow, MLflow, Composer), and cloud platforms (AWS, GCP, Azure).
• Strong knowledge of telecom domain data structures and analytics requirements.
• Experience designing scalable distributed systems and data architectures for telecom datasets.
• Proficiency in Python, containerization (Docker), and orchestration (Kubernetes).
• Excellent analytical, architectural, problem-solving, and communication skills.
• Ability to bridge technical and telecom domain knowledge effectively.
• Understanding the Hadoop & GCP Bigdata architecture – DataProc, Vertex.AI, BigQuery, Composer,Jenkins,

Constructing the Common packages like products / bundles is an advantage