Data Scientist

Three Across; Gurugram District; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Three Across is looking for an experienced Data Scientist to design, develop, and deploy advanced AI systems utilizing Generative AI and Large Language Models (LLMs). This role will involve creating scalable AI applications that address complex business challenges.

The ideal candidate will have a strong background in AI frameworks, cloud solutions on AWS, and skills in system design and architecture. Experience with RAG architectures and collaborative work with stakeholders is essential for success in this position.

Qualifications

• Strong experience in Generative AI and Large Language Models (LLMs).
• Hands-on expertise with Amazon Bedrock and AWS cloud ecosystem.
• Experience designing and implementing Retrieval-Augmented Generation (RAG) architectures.
• Proficiency in SQL for data analysis and querying.

Responsibilities

• Design and develop end-to-end Generative AI solutions.
• Architect scalable AI systems focusing on reliability and performance.
• Collaborate with stakeholders to translate business requirements into AI-driven solutions.

Skills

Generative AI

Large Language Models (LLMs)

Amazon Bedrock

Retrieval-Augmented Generation (RAG)

Python

SQL

AWS cloud ecosystem

Data processing workflows

Tools

Docker

Kubernetes

Job description

We are seeking an experienced Data Scientist with strong expertise in Generative AI, Agentic AI frameworks, and cloud-native AI solution development. The ideal candidate will be responsible for designing, developing, and deploying advanced AI systems leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Agentic AI architectures on AWS.

This role requires a blend of data science, machine learning, software engineering, and system design capabilities to build scalable and production-ready AI applications that solve complex business challenges.

Key Responsibilities

• Design and develop end-to-end Generative AI solutions leveraging Large Language Models (LLMs) and Agentic AI frameworks.
• Architect scalable AI systems with a strong focus on system design, reliability, performance, and maintainability.
• Build and optimize Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge management and intelligent automation use cases.
• Develop autonomous and semi-autonomous AI agents capable of task planning, tool usage, memory management, and workflow orchestration.
• Leverage AWS AI and cloud-native services, including Amazon Bedrock, for model deployment, orchestration, and enterprise-scale AI implementations.
• Design and implement data pipelines, data processing workflows, and model-serving architectures.
• Collaborate with product managers, engineers, architects, and business stakeholders to translate business requirements into AI-driven solutions.
• Evaluate and benchmark foundation models, embeddings, vector databases, and prompt engineering techniques.
• Ensure adherence to best practices around model governance, security, scalability, observability, and responsible AI principles.
• Conduct experimentation, model evaluation, performance tuning, and continuous improvement of deployed AI systems.
• Create technical documentation, architectural artifacts, and implementation guidelines.

Mandatory Technical Skills

• Strong experience in Generative AI and Large Language Models (LLMs).
• Hands-on expertise with Amazon Bedrock and AWS cloud ecosystem.
• Experience designing and implementing Retrieval-Augmented Generation (RAG) architectures.
• Strong understanding and practical experience with Agentic AI frameworks and autonomous agent development.
• Experience working with frameworks such as LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, or similar Agentic AI platforms.
• Strong system design and architecture capabilities for enterprise-scale AI solutions.
• Proficiency in SQL for data analysis, querying, and data validation.
• Experience with vector databases, embeddings, semantic search, and knowledge retrieval systems.
• Hands-on experience with Python and modern AI/ML development ecosystems.
• Experience deploying AI solutions in cloud-native environments.

Preferred Skills

• Experience with multi-agent systems and workflow orchestration.
• Knowledge of MLOps, CI/CD, model monitoring, and deployment automation.
• Experience with containerization technologies such as Docker and Kubernetes.
• Exposure to API development and integration frameworks.
• Understanding of AI governance, responsible AI, and model evaluation methodologies.
• Experience working in consulting, digital transformation, or enterprise AI engagements.