Role Overview:
We are seeking a highly skilled and motivated Senior Data Scientist with deep
expertise in Generative AI, Machine Learning, Deep Learning, and
advanced Data Analytics. The ideal candidate will have hands-on experience in
building, deploying, and maintaining end-to-end ML solutions at scale, preferably
within the Telecom domain.
You will be part of our AI & Data Science team, working on high-impact projects
ranging from customer analytics, network intelligence, churn prediction, to generative
AI applications in telco automation and customer experience.
Key Responsibilities:
Design, develop, and deploy advanced machine learning and deep learning
models for Telco use cases such as:
o Network optimization
o Customer churn prediction
o Usage pattern modeling
o Fraud detection
o GenAI applications (e.g., personalized recommendations,
customer service automation)
Lead the design and implementation of Generative AI solutions (LLMs,
transformers, text-to-text/image models) using tools like OpenAI, Hugging
Face, LangChain, etc.
Collaborate with cross-functional teams including network, marketing, IT, and
business to define AI-driven solutions.
Perform exploratory data analysis, feature engineering, model selection, and
evaluation using real-world telecom datasets (structured and unstructured).
Drive end-to-end ML solution deployment into production (CI/CD pipelines,
model monitoring, scalability).
Optimize model performance and latency in production, especially for real-
time and edge applications.
Evaluate and integrate new tools, platforms, and AI frameworks to advance
Vi’s data science capabilities.
Provide technical mentorship to junior data scientists and data engineers.
Required Qualifications & Skills:
6–9 years of industry experience in Machine Learning, Deep Learning,CNN’S,
and Advanced Analytics.
Strong hands-on experience with GenAI models and frameworks (e.g.,
GPT, BERT, Llama, LangChain, RAG pipelines).
Proficiency in Python, and libraries such as scikit-learn, TensorFlow,
PyTorch, Hugging Face Transformers, etc.
C2 – Vodafone Idea Internal
Experience in end-to-end model lifecycle management,
from data preprocessing to production deployment (MLOps).
Familiarity with cloud platforms like AWS, GCP, or Azure; and ML
deployment tools (Docker, Kubernetes, MLflow, FastAPI, etc.).
Strong understanding of SQL, big data tools (Spark, Hive), and data pipelines.
Excellent problem-solving skills with a strong analytical mindset and business
acumen.
Prior experience working on Telecom datasets or use cases is a strong plus.
Preferred Skills:
Experience with vector databases, embeddings, and retrieval-augmented
generation (RAG) pipelines.
Exposure to real-time ML inference and streaming data platforms (Kafka,
Flink).
Knowledge of network analytics, geo-spatial modeling, or customer
behavior modeling in a Telco environment.
Experience mentoring teams or leading small AI/ML projects.
Job Title: Senior MLOps + DevOps Engineer (On-Prem AI Platform)
Role Overview:
We are looking for a Senior MLOps + DevOps Engineer (8+ years) to architect, build, and
scale AI/ML platforms in an on-prem enterprise environment.
This role requires end-to-end ownership of ML systems, infrastructure, CI/CD, and
production reliability, enabling scalable deployment of machine learning and GenAI
solutions.
Key Responsibilities:
1. Platform Architecture & Ownership
- Design and own end-to-end ML platform architecture (data → training → deployment →
monitoring)
- Define and enforce best practices for scalable and secure ML systems
- Standardize MLOps + DevOps frameworks and processes
2. Model Deployment & Serving
- Deploy and manage ML/LLM models on GPU-based on-prem infrastructure
- Optimize inference performance (latency, throughput, batching)
- Implement model versioning, A/B testing, and rollback strategies
3. CI/CD & Automation
- Design and implement CI/CD pipelines for ML models, APIs, and data workflows
- Enable automated testing, deployment, and release management
4. Infrastructure & Containerization
- Manage Linux-based (RHEL preferred) on-prem infrastructure
- Containerize applications using Docker
- Deploy and orchestrate workloads using Kubernetes / OpenShift
- Operate within restricted or air-gapped environments
5. Data & System Integration
- Build pipelines integrating structured databases and high-volume logs/streaming data
- Support batch and real-time inference architectures
6. Monitoring, Observability & Reliability
- Implement end-to-end observability (model + infra)
- Use tools like Prometheus, Grafana, ELK stack
- Ensure high availability, SLA adherence, and incident response
7. GenAI & Advanced ML Systems
- Deploy RAG pipelines and vector databases
- Manage LLM serving frameworks
- Work with agent orchestration frameworks
8. Leadership & Collaboration
- Mentor engineers on MLOps and DevOps best practices
- Collaborate with cross-functional teams
- Drive design reviews and production readiness
Required Skills:
- Strong Python and scripting (Bash)
- Deep understanding of ML lifecycle and productionization
- Experience deploying ML/LLM systems in production
- Linux, Docker, Kubernetes/OpenShift
- CI/CD tools (Jenkins/GitLab CI)
- SQL and data pipeline experience
Good to Have:
- GPU optimization knowledge
- MLflow / Kubeflow
- Terraform / Ansible
- Experience in on-prem or restricted environments
Experience:
- 8+ years in MLOps / DevOps / Platform Engineering
- Proven experience scaling production ML systems
Ideal Candidate:
A hands-on platform architect who can operate across ML systems and infrastructure,
driving automation, scalability, and reliability.