Data Scientist

Prodapt Solutions; Bengaluru; On-site; INR 1,500,000 - 2,100,000; Full time

Job summary

Prodapt Solutions in Bengaluru is seeking a Data Scientist with 5+ years of ML/GenAI experience to drive an enterprise analytics and AI transformation in the telecom sector. Experience in telecom is preferred but not required; strong candidates from other industries are encouraged to apply.

You will design, build, evaluate, and deploy ML models, develop GenAI and RAG applications, and collaborate with engineering and business teams to deliver production-ready AI.

Qualifications

• 5+ years of hands-on DS and ML experience with end-to-end deployment.
• GenAI experience (1+ year) including RAG and LLM apps.
• Proficiency in Python, SQL, pandas, NumPy, and scikit-learn.

Responsibilities

• Design, build, evaluate, and deploy ML models and GenAI apps.
• Develop RAG-based, embeddings and vector search solutions.
• Collaborate with engineering, platform, and business teams for production-ready AI.
• Translate business needs into measurable AI use cases and communicate results.

Skills

Python

SQL

pandas

NumPy

scikit-learn

GenAI frameworks

RAG/LLM applications

REST APIs

Cloud environments

MLOps

Stakeholder communication

Education

Bachelor's or Master's in a quantitative field

Tools

LangChain

LlamaIndex

LangGraph

Job description

Overview

We are hiring a Data Scientist with 5+ years of experience in Machine Learning and Generative AI to support an enterprise analytics and AI transformation project in the telecommunications industry. Telecom experience is preferred but not mandatory, and strong candidates from other industries are encouraged to apply.

Responsibilities

Role Purpose The successful candidate will design, build, evaluate, and deploy ML models and GenAI applications that solve real business problems. The work spans predictive analytics on structured and unstructured data, LLM-powered applications with Retrieval-Augmented Generation (RAG), and production-ready AI solutions delivered in close collaboration with engineering, platform, and business teams.

Key Responsibilities

• Data Science ML: Build and validate predictive models (classification, regression, clustering, forecasting, anomaly detection) and operationalize them through scalable pipelines.
• Generative AI: Develop enterprise GenAI applications using leading LLMs, implementing RAG, embeddings, vector search, and prompt engineering, with attention to accuracy, groundedness, and cost.
• Agentic AI (strong advantage): Build tool-enabled, multi-step AI agents using frameworks such as LangGraph, CrewAI, or Google ADK, with human-in-the-loop controls and reliability monitoring.
• MLOps / LLMOps: Move solutions from experimentation to production, covering APIs, versioning, monitoring, CI/CD, and cloud AI platforms (AWS Bedrock, Azure AI Foundry, Google Vertex AI).
• Stakeholder Engagement: Translate business needs into measurable AI use cases and communicate results and limitations clearly to technical and non-technical audiences.

Requirements

Key Requirements

• 5+ years of hands-on Data Science and ML experience, including end-to-end model deployment
• Practical GenAI experience (1+ years preferred), including RAG and LLM application development
• Strong Python, SQL, pandas, NumPy, and scikit-learn skills
• Experience with at least one GenAI framework (LangChain, LlamaIndex, LangGraph, or equivalent)
• Familiarity with REST APIs, cloud environments, and ML deployment practices
• Bachelor's or Master's degree in a quantitative discipline

Preferred Qualifications

• Agentic AI development experience and familiarity with MCP or similar integration patterns
• Exposure to fine-tuning and cost-efficient LLM inference
• Understanding of AI guardrails, evaluation frameworks, and responsible AI practices
• Telecom industry experience, particularly in churn, billing, network analytics, or customer experience

Ideal Candidate

A hands-on data scientist with strong ML foundations, proven GenAI and RAG delivery experience, a production engineering mindset, and the ability to adapt to new business domains.