About Prismforce
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
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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
