AGM-Data Scientist

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.