Interesting Job Opportunity: Generative AI Engineer - LLM

Prismforce; Bengaluru; On-site; INR 1,400,000 - 2,200,000; Full time

Job summary

Prismforce in Bengaluru seeks an experienced Gen AI/NLP engineer to fine-tune LLMs, enhance RAG pipelines, and apply transfer learning across transformer architectures. You will validate models with robust metrics and collaborate with product teams to present insights to leadership.

The role emphasizes experimentation, data-driven dataset construction, and scalable ML/NLP solutions, with startup mindset and innovative thinking.

Qualifications

• Bachelor's degree in a quantitative field or equivalent experience
• 3-5 years hands-on Gen AI and NLP
• Prior startup/high-growth experience is a plus

Responsibilities

• Fine-tune LLMs using techniques like LoRA and QLoRA
• Evaluate and improve RAG 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

Skills

NLP techniques

LLMs

RAG pipelines

Python

TensorFlow

PyTorch

Hugging Face

OpenAI

Transfer learning

Fine-tuning

Education

Bachelor's degree in quantitative field or equivalent

Tools

TensorFlow

PyTorch

Hugging Face

OpenAI API

Job description

• 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

Description

• 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:

• Bachelors 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, Tensorflow