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: -
- 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: -
- 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
