Data Scientist

Experience: 2–6 years
Location: Bangalore
Employment Type: Full-time
Staff Level: T8/T9

Responsibilities:

• Design and develop scalable applications leveraging Generative AI technologies for software process automation and intelligent data retrieval.
• Build and optimize RAG (Retrieval-Augmented Generation) pipelines using LLMs and VLMs to enhance chatbot capabilities and contextual understanding.
• Collaborate with cross-functional teams to deploy AI-powered solutions on Azure cloud infrastructure.
• Integrate Python-based backend services with cloud-native tools for seamless deployment and monitoring.
• Continuously evaluate and improve application performance, security, and reliability in production environments.

Required Skillsets:

• GenAI Expertise: Hands-on experience with LLMs (e.g., GPT, LLama), VLMs, and RAG architecture.
• Programming: Strong proficiency in Python, including frameworks like FastAPI, Lang Chain, or similar. Need solid skills using Pyspark for processing big data (efficient data transformations)
• Cloud & DevOps: Experience deploying and managing applications on Azure, including Azure Functions, App Services, and Azure AI services.
• Database & Retrieval: Familiarity with vector databases (e.g., ChromaDB, Pinecone) and efficient data retrieval techniques.
• Software Engineering: Solid understanding of software development lifecycles, version control (Git), and CI/CD pipelines.

Experience: 2–6 years
Locatio Experience: 2–6 years
Location: Bangalore
Employment Type: Full-time
Staff Level: T8/T9

Responsibilities:

• Design and develop scalable applications leveraging Generative AI technologies for software process automation and intelligent data retrieval.
• Build and optimize RAG (Retrieval-Augmented Generation) pipelines using LLMs and VLMs to enhance chatbot capabilities and contextual understanding.
• Collaborate with cross-functional teams to deploy AI-powered solutions on Azure cloud infrastructure.
• Integrate Python-based backend services with cloud-native tools for seamless deployment and monitoring.
• Continuously evaluate and improve application performance, security, and reliability in production environments.

Required Skillsets:

• GenAI Expertise: Hands-on experience with LLMs (e.g., GPT, LLama), VLMs, and RAG architecture.
• Programming: Strong proficiency in Python, including frameworks like FastAPI, Lang Chain, or similar. Need solid skills using Pyspark for processing big data (efficient data transformations)
• Cloud & DevOps: Experience deploying and managing applications on Azure, including Azure Functions, App Services, and Azure AI services.
• Database & Retrieval: Familiarity with vector databases (e.g., ChromaDB, Pinecone) and efficient data retrieval techniques.
• Software Engineering: Solid understanding of software development lifecycles, version control (Git), and CI/CD pipelines.n: Bangalore
  Employment Type: Full-time
  Staff Level: T8/T9

Responsibilities:

• Design and develop scalable applications leveraging Generative AI technologies for software process automation and intelligent data retrieval.
• Build and optimize RAG (Retrieval-Augmented Generation) pipelines using LLMs and VLMs to enhance chatbot capabilities and contextual understanding.
• Collaborate with cross-functional teams to deploy AI-powered solutions on Azure cloud infrastructure.
• Integrate Python-based backend services with cloud-native tools for seamless deployment and monitoring.
• Continuously evaluate and improve application performance, security, and reliability in production environments.

Required Skillsets:

• GenAI Expertise: Hands-on experience with LLMs (e.g., GPT, LLama), VLMs, and RAG architecture.
• Programming: Strong proficiency in Python, including frameworks like FastAPI, Lang Chain, or similar. Need solid skills using Pyspark for processing big data (efficient data transformations)
• Cloud & DevOps: Experience deploying and managing applications on Azure, including Azure Functions, App Services, and Azure AI services.
• Database & Retrieval: Familiarity with vector databases (e.g., ChromaDB, Pinecone) and efficient data retrieval techniques.
• Software Engineering: Solid understanding of software development lifecycles, version control (Git), and CI/CD pipelines.