GM - IT Analytics

Vodafone Idea Limited; Pune District; On-site; INR 2,800,000 - 6,000,000; Full time

Job summary

Vodafone Idea Limited is seeking an experienced AI Architect to design and scale enterprise-grade AI and GenAI solutions in a modern data environment. You will define the technical architecture and roadmap for AI applications built on Cloudera Data Platform (CDP 7.1.9 / 7.3.1), bridging big data ecosystems with ML/LLM workflows.

The role requires hands-on model selection, governance, prompt engineering, and production-grade MLOps integrated into DevOps pipelines.

Qualifications

• Bachelor's or Master's in Computer Science, Data Science, AI or related field.
• 10+ years in Data Science / Software Engineering, with 4+ years as AI/ML Architect delivering enterprise-scale systems.
• Deep hands-on experience with CDP 7.1.9 or CDP 7.3.1, PySpark, HDFS, Hive/Impala.
• Proven experience developing, fine-tuning and deploying LLMs, RAG architectures, multi-agent frameworks, and traditional ML models.

Responsibilities

• Lead end-to-end AI architecture and integration on CDP-based infrastructure.
• Design high-throughput, low-latency data pipelines and feature stores using CDP components (Spark, Hive, Impala, Kafka, Iceberg).
• Establish retrieval-augmented generation (RAG) frameworks and vector search systems for enterprise data.
• Implement robust MLOps/AIOps pipelines with CI/CD/CT and monitoring.

Skills

AI/ML architecture

Big data

MLOps

Python

PySpark

Kubernetes

Docker

Governance

Prompt engineering

Education

Bachelor's or Master's in CS/Data Science/AI

Tools

Docker

Kubernetes

Jenkins

GitLab CI

GitHub Actions

Terraform

Ansible

MLflow

Weights & Biases

Job description

Position Overview

Vodafone Idea is looking for a seasonedAI Architectto design, build, and scale enterprise-grade AI and Generative AI (GenAI) solutions. In this role, you will be responsible for defining the technical architecture and roadmap for AI applications deployed on top of our enterpriseCloudera Data Platform (CDP 7.1.9 / 7.3.1).

You will bridge the gap between complex big data ecosystems and cutting-edge machine learning/LLM workflows. The ideal candidate brings hands‑on expertise in model selection, lifecycle governance, prompt engineering, and production‑grade MLOps/AIOps integrated into modern DevOps pipelines.

Key Responsibilities

1. Enterprise AI & CDP Solution Architecture

• Lead the end‑to‑end architecture and integration of Predictive AI and Generative AI applications built directly onCDP 7.1.9 / 7.3.1infrastructure.
• Design high‑throughput, low‑latency data pipelines and feature stores leveraging CDP components (Spark, Hive, Impala, Kafka, Apache Iceberg).
• Establish retrieval‑augmented generation (RAG) frameworks, fine‑tuning setups, and vector search systems connected to enterprise big data repositories.

2. Model Selection, Implementation & Governance

• Evaluate, benchmark, and select optimal foundational models (LLMs, SLMs, open‑source vs. proprietary) and classic ML algorithms tailored for telecom use cases (e.g., customer churn, network optimization, hyper‑personalization, fraud detection).
• Enforce robust AI Governance frameworks covering model drift detection, explainability (XAI), fairness/bias mitigation, data privacy, and compliance.
• Manage model registry, lifecycle versioning, and policy enforcement across dynamic runtime environments.

3. Prompt Engineering & GenAI Optimization

• Establish standardized prompt engineering methodologies, context‑window management, and agentic workflows (e.g., LangChain, LlamaIndex, AutoGen).
• Optimize token utilization, latency, throughput, and inference costs for LLM deployments.
• Implement guardrails, output validation, and safety filters to prevent hallucination, data leakage, and security risks in public/private LLM interactions.

4. MLOps, AIOps & DevOps Integration

• Design and implement robust MLOps/AIOps pipelines for automated model training, continuous integration, testing, deployment, and real‑time monitoring (CI/CD/CT).
• Partner with DevOps teams to deploy containerized AI microservices (Kubernetes, Docker) within hybrid‑cloud and on‑premises infrastructure.
• Configure comprehensive telemetry, logging, and observability tools (Prometheus, Grafana, MLflow, Weights & Biases) to maintain continuous operational health.

Required Qualifications & Experience

• Education:Bachelors or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field.
• Experience:10+ years in Data Science / Software Engineering, with at least4+ years as an AI/ML Architectdelivering enterprise scale systems.
• Big Data Platform:Deep hands‑on experience withCloudera Data Platform (CDP 7.1.9 or CDP 7.3.1), PySpark, HDFS, and Hive/Impala.
• AI & GenAI Expertise:Proven experience developing, fine‑tuning, and deploying Large Language Models (LLMs), RAG architectures, multi‑agent frameworks, and traditional supervised/unsupervised ML models.
• MLOps & Governance:Strong experience with MLflow, Model Registry, feature stores, model drift monitoring, data lineage, and enterprise AI guardrails.
• DevOps Tools:Proficiency with Docker, Kubernetes, CI/CD tools (Jenkins, GitLab CI, GitHub Actions), Infrastructure‑as‑Code (Terraform, Ansible), and Git workflows.
• Programming Languages:Advanced expertise in Python, PySpark, SQL, and bash scripting.
• Frameworks & Libraries:PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, vLLM, Triton Server.

Preferred Qualifications

• Prior experience in Telecom / Communication Service Provider (CSP) domain handling massive scale subscriber and event network data.