Gen AI Scientist

2 days ago

Bengaluru, Karnataka, India ITC Infotech Full time ₹ 12,00,000 - ₹ 36,00,000 per year

Gen AI Scientist

Location: Mumbai

Mode: 5 Days Work From Office

Mandatory Skill:

Gen AI:
Langchain, Ollama (Optional), Prompt Engineering, RAG, Fine-Tuning,

Agentic AI:
LangGraph, CrewAI, Agent orchestration development & Integration

LLM:
OpenAI, Gemini, Mistral, LLaMA

IDE:
VS code

DS Libraries:
Pandas, NumPy, Sci-kit Learn, Matplotlib, Seaborn

Cloud:
Azure

API:
FastAPI

Job Description:

  • 5+ years of experience in data science and machine learning, with at least 2 years specifically in Generative AI or LLMs.
  • Assist in identification of AI use cases and develop AI tools for successful prototypes.
  • Design and deploy AI-powered solutions aimed at enhancing operational efficiency, supporting data-driven decision-making, and elevating customer engagement.
  • This includes, but is not limited to, the development of AI models, automation of IT operations, seamless integration with BFSI (Banking, Financial Services, and Insurance) applications, compliance with regulatory standards, and implementation of scalable, cloud-based AI infrastructure.
  • Execute LLMOps activities such as data preparation, model training, model deployment, and model monitoring.
  • Facilitate the seamless transition of the voice and vernacular transactions project from the incumbent IT service provider, ensuring continuity and minimal disruption.
  • Implement WhatsApp integration to enable both customer-initiated and distributor-initiated transactions, enhancing communication and service accessibility.
  • Collaborate with Customer to explore and identify additional opportunities for automation and AI-driven enhancements across business functions.

GenAI: L2, L3 Support:

  • Troubleshooting for any breakdown in GenAI application.
  • Support for any LLM's response deviation, prompt update & further prompt customization.
  • Backend support for code update for dependent framework.
  • Support for any breakdown in data source, deployment pipeline.
  • 8x5 support model

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