AGM-Data Scientist
Hyderabad, IN
Role Overview:
We are looking for a hands-on Data Engineer with 8+ years of experience to build, manage, and scale data pipelines, deploy ML solutions, and enable advanced data visualizations and dashboards for business consumption. The ideal candidate will have a strong engineering mindset, deep understanding of data infrastructure, and prior experience working on self-managed or private cloud (VM-based) deployments.
Candidates from premier institutes (IITs, NITs, or equivalent Tier-1/2 schools) are strongly preferred.
Key Responsibilities:
-
Design and build robust, scalable, and secure data pipelines (batch and real-time) to support AI/ML workloads and BI dashboards.
-
Collaborate with data scientists to operationalize ML models, including containerization (Docker), CI/CD pipelines, model serving (FastAPI/Flask), and monitoring.
-
Develop and maintain interactive dashboards using tools such as Plotly Dash, Power BI, or Streamlit to visualize key insights for business stakeholders.
-
Manage deployments and orchestration on Vi’s local private cloud infrastructure (VM-based setups).
-
Work closely with analytics, business, and DevOps teams to ensure reliable data availability and system health.
-
Optimize ETL/ELT workflows for performance and scale across large telecom datasets.
-
Implement data quality checks, governance, and logging/monitoring solutions for all production workloads.
Required Qualifications & Skills:
-
8+ years of experience in data engineering, platform development, and/or ML deployment.
-
Prefered B.Tech/M.Tech from Tier-1 or Tier-2 institutes (IITs, NITs, IIITs, BITS, etc.).
-
Strong proficiency in Python, SQL, and data pipeline frameworks (Airflow, Luigi, or similar).
-
Solid experience with containerization (Docker), scripting, and deploying production-grade ML or analytics services.
-
Hands-on experience with dashboarding and visualization tools such as:
-
Power BI / Tableau / Streamlit
-
Custom front-end dashboards (nice to have)
-
-
Experience working on self-managed VMs, bare-metal servers, or local private clouds (not just public cloud services).
-
Familiarity with ML deployment architectures, REST APIs, and performance tuning.
Preferred Skills:
-
Experience with Kafka, Spark, or distributed processing systems.
-
Exposure to MLOps tools (MLflow, DVC, Kubeflow).
-
Understanding of telecom data and analytics use cases.
-
Ability to lead and mentor junior engineers or analysts.