Subcontractor - Chennai, Tamil Nadu, India - BirlaSoft

BirlaSoft

Verified Company

Chennai, Tamil Nadu, India

2 weeks ago

Posted by:

Deepika Kaur

beBee Recuiter

Freelance

Description

Country/Region:

IN- Requisition ID: 7390- Work Model:

Position Type:
Salary Range:

Location: INDIA

CHENNAI
BIRLASOFT OFFICE
Title:Subcontractor**:
Description:
Must have 10+ years of experience working in Data science, Machine learning and especially NLP technologies.
Exposure to various LLM technologies and solid understanding of Transformer Encoder Networks.
Utilize your extensive knowledge and expertise in machine learning (ML) with a focus on generative models, including but not limited to generative adversarial networks (GANs), variational autoencoders (VAEs), and transformerbased architectures.
Solid understanding of Model development, model serving, training/retraining techniques in a data sparse environment.
Very good understanding of Prompt engineering techniques in developing Instruction based LLMs.
Must be able to design, and implement stateoftheart generative models for natural language processing (NLP) tasks such as text generation, text completion, language translation, and document summarization.
Work with SAs and collaborate with crossfunctional teams to identify business requirements and deliver solutions that meet the customer needs.
Passionate to learn and stay updated with the latest advancements in generative AI and LLM.
Nice to have contributions to the research community through publications, presentations, and participation in relevant conferences or workshops.
Evaluate and preprocess largescale datasets, ensuring data quality and integrity, and develop data pipelines for training and evaluation of generative models.
Ability to articulate to business stakeholders on the hallucination effects and various model behavioral analysis techniques followed.
Exposure to developing Guardrails for LLMs both with open source and cloud native models.
Collaborate with software engineers to deploy and optimize generative models in production environments, considering factors such as scalability, efficiency, and realtime performance.
Nice to have

- provide guidance to junior data scientists, sharing expertise and knowledge in generative AI and LLM, and contribute to the overall growth and success of the data science team.

Must have 10+ years of experience working in Data science, Machine learning and especially NLP technologies.
Exposure to various LLM technologies and solid understanding of Transformer Encoder Networks.
Utilize your extensive knowledge and expertise in machine learning (ML) with a focus on generative models, including but not limited to generative adversarial networks (GANs), variational autoencoders (VAEs), and transformerbased architectures.
Solid understanding of Model development, model serving, training/retraining techniques in a data sparse environment.
Very good understanding of Prompt engineering techniques in developing Instruction based LLMs.
Must be able to design, and implement stateoftheart generative models for natural language processing (NLP) tasks such as text generation, text completion, language translation, and document summarization.
Work with SAs and collaborate with crossfunctional teams to identify business requirements and deliver solutions that meet the customer needs.
Passionate to learn and stay updated with the latest advancements in generative AI and LLM.
Nice to have contributions to the research community through publications, presentations, and participation in relevant conferences or workshops.
Evaluate and preprocess largescale datasets, ensuring data quality and integrity, and develop data pipelines for training and evaluation of generative models.
Ability to articulate to business stakeholders on the hallucination effects and various model behavioral analysis techniques followed.
Exposure to developing Guardrails for LLMs both with open source and cloud native models.
Collaborate with software engineers to deploy and optimize generative models in production environments, considering factors such as scalability, efficiency, and realtime performance.
Nice to have

- provide guidance to junior data scientists, sharing expertise and knowledge in generative AI and LLM, and contribute to the overall growth and success of the data science team.