Lead Data Science & ML Ops - ML migration

Prodapt; Chennai District; On-site; INR 18,00,000 - 25,00,000; Full time

Job summary

A telecom solutions company in Chennai is seeking an experienced ML Architect to design and oversee ML and data science systems for large-scale telecom environments. This role requires deep expertise in ML frameworks and MLOps tools, alongside strong knowledge of telecom data structures. Responsibilities include defining MLOps strategies, collaborating with data teams, and ensuring the scalability and reliability of telecom ML systems.

Qualifications

• Deep expertise in ML frameworks like TensorFlow and PyTorch.
• Strong knowledge of telecom domain data structures.
• Experience with scalable distributed systems for telecom datasets.

Responsibilities

• Architect end-to-end ML and DS solutions for telecom.
• Define and implement MLOps strategies for telecom models.
• Collaborate with teams to optimize telecom data pipelines.

Skills

ML frameworks (TensorFlow, PyTorch)

MLOps tools (Kubeflow, MLflow, Composer)

Cloud platforms (AWS, GCP, Azure)

Python

Containerization (Docker)

Orchestration (Kubernetes)

Telecom domain data structures

Hadoop & GCP Bigdata architecture

Job description

Overview

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.