GEN AI Data Scientist

Cognizant; Hyderabad, Bengaluru, Chennai District; Hybrid; INR 15,00,000 - 25,00,000; Full time

Job summary

A global technology company is seeking a Gen AI Data Scientist to design and develop backend systems integrating AI technologies. The ideal candidate will have 5-12 years of experience, proficiency in Python, and expertise in AI/ML orchestration. This role involves developing REST APIs, managing data pipelines, and collaborating across teams to implement generative AI solutions across applications.

Qualifications

• 5 - 12 years of experience in a data-related role.
• Strong expertise in Python for backend development.
• Proficient in building and managing data pipelines.

Responsibilities

• Design and develop REST APIs using Python for AI services.
• Implement machine learning models and AI orchestration.
• Collaborate with teams to embed Gen AI features.

Skills

Python

AIML

NLP

Machine Learning

Data Science

Gen AI

AWS

Azure

Tools

Databricks

Azure Data Factory

CosmosDB

Elasticsearch

Job description

Role: Gen AI Data Scientist

Location: PAN India

Experience: 5 - 12 years

Skills: Python, AIML, NLP, Machine Learning, Data Science, Gen AI, AWS, Azure

Key Responsibilities:

• Backend Development: Design, develop, and maintain REST APIs using Python to integrate AI/ML services.
• Agentic AI Development: Design and orchestrate AI agents capable of reasoning, planning, and executing multi-step tasks by integrating LLMs with APIs, tools, and data sources.
• AI/ML Orchestration: Implement and manage machine learning models, large language models (LLMs), Agentic AI workflows and AI orchestration using Python.
• Generative AI Solutions: Using Generative AI models for tasks like text generation, summarization, conversational AI, and content creation.
• Data Management: Work with structured databases (SQL), graph databases (e.g., CosmosDB), and unstructured data stores (e.g., Elasticsearch).
• RAG Implementation: Build retrieval-augmented generation (RAG) pipelines leveraging Azure AI Search, AWS OpenSearch, or other vector databases for contextual responses.
• Data Pipelines: Design and manage robust data ingestion and transformation pipelines to feed AI models.
• Intent Detection & NLU: Develop or integrate intent detection models and natural language understanding (NLU) solutions to enhance conversational AI.
• Prompt Engineering & Optimization: Create and optimize prompts for LLMs to improve response quality and reduce latency.
• AI Integration: Collaborate with frontend and product teams to embed Gen AI features into enterprise applications.

Required Skills:

• Backend Development: Proficient in Python for building and maintaining scalable REST APIs, familiarity with integrating AI services.
• AI/ML Orchestration: Strong expertise in Python with a focus on machine learning, large language models (LLMs), AI orchestration.
• Agentic AI Expertise: Experience in building autonomous AI agents using frameworks like OpenAI Functions, or custom orchestration solutions to handle tool use and multi-step reasoning.
• Generative AI Expertise: Generative AI models (text generation, summarization, conversational AI) and applying prompt engineering techniques.
• Data Management: Solid understanding of structured (SQL), graph (CosmosDB), and unstructured (Elasticsearch) databases; ability to design efficient data access patterns for AI workloads.
• RAG Implementation: Proven experience implementing retrieval-augmented generation using Azure AI Search, AWS OpenSearch, or other vector databases.
• Data Pipelines: Hands-on experience building and managing data ingestion and transformation pipelines using Databricks, Azure Data Factory, or equivalent tools.
• Intent Detection & NLU: Skilled in developing or deploying intent detection models and natural language understanding (NLU) components for conversational AI applications.

Preferred Skills:

• Familiarity with cloud platforms such as Azure and AWS.
• Knowledge of additional AI/ML frameworks and tools.
• Knowledge of Agentic AI
• Experience with DevOps practices and CI/CD pipelines.