Generative ai Data Scientist I / II

Prismforce is a Vertical SaaS company revolutionizing the Talent Supply Chain for global Technology,

R&D/Engineering, and IT Services companies. Our AI-powered product suite enhances business

performance by enabling operational flexibility, accelerating decision-making, and boosting

profitability. Our mission is to become the leading industry cloud/SaaS platform for tech services and

talent organizations worldwide.

We’re looking for a passionate and curious Generative AI Engineer to join our team in Bangalore.

You’ll work on cutting-edge NLP and ML projects, leveraging large language models (LLMs) and

advanced deep learning techniques to solve complex business challenges.

Job Description

Role: Gen AI Engineering

Reporting to: Data Scientist

Location: Bangalore

Exp: 2-5 Years

Key Responsibilities

 Fine-tune LLMs using techniques like LoRA and QLoRA

 Evaluate and improve RAG (Retrieval-Augmented Generation) pipelines for

groundedness, accuracy, and relevance

 Apply transfer learning and transformer architectures in model development

 Validate model accuracy and performance using appropriate metrics

 Collaborate with product teams and communicate insights to senior leadership

 Participate in problem-solving sessions and contribute innovative ideas

 Maintain an experimental mindset and continuously explore new approaches

 Identify and integrate relevant data sources to build meaningful datasets

 Automate data collection and preprocessing for structured and unstructured data

 Handle large-scale data to feed analytical and predictive models

 Build and optimize machine learning and deep learning models, including NLP

solutions

Requirements

Education & Experience

 Bachelor’s degree in a quantitative field (Computer Science, Engineering, Physics,

Mathematics, Operations Research) or equivalent experience

 3-5 years of hands-on experience in Gen AI and NLP

 Prior experience in startups or high-growth environments is a plus

• Technical Skills  Deep expertise in NLP techniques: text generation, sentiment analysis, NER, and language modeling  Hands-on experience with LLMs and RAG pipelines  Proficiency in neural network frameworks: TensorFlow, PyTorch  Familiarity with transformer architecture and transfer learning  Fluency in at least one programming language: Python, R, or Julia  Experience with Gen AI libraries: Hugging Face, OpenAI, etc.  Strong foundation in ML algorithms: supervised, unsupervised, reinforcement learning, Bayesian inference  Fine Tuning, Transfer Learning, Pytorch, TensorflowAnalytical