Data Scientist-I/II

Prismforce; Bengaluru; Hybrid; INR 1,200,000 - 2,100,000; Full time

Benefits offered by this job

Group medical cover

Hybrid work and flexible policies

Self-driven career progression

Job summary

Prismforce is hiring for an AI/ML scientist to fine-tune transformers (LoRA/QLoRA) for entity extraction and classification, build knowledge graphs, and deploy scalable retrieval and ranking pipelines. You will work with large-scale data from global tech services clients, delivering production-ready models and tooling.

You will collaborate with the AI & ML team to design experiments, evaluate model performance, and iterate on models with a strong emphasis on real-world impact and scalability.

Qualifications

• Hands-on NLP or ML experience: 1-3 years for Data Scientist I, 3-5 years for Data Scientist II.
• Strong Python, with PyTorch, Hugging Face Transformers, pandas and scikit-learn.
• Has fine-tuned transformer models on their own data, such as an encoder for NER or classification, or a decoder LLM with PEFT (LoRA/QLoRA).
• Solid grasp of embeddings, attention, contrastive training, and retrieval with bi-encoders and cross-encoders.
• Working knowledge of classical ML: gradient boosting, logistic regression, clustering, and dimensionality reduction.
• Comfort with statistics and evaluation design: sampling, significance, calibration and metric choice.
• SQL, plus the ability to explore data at the scale of millions of records.
• Clear written communication of experiments and results.
• Experience with learning-to-rank, knowledge graphs or large taxonomies in a real product.
• Experience with ML lifecycle tooling: experiment tracking, model versioning, Docker, and GPU training and serving on AWS.

Responsibilities

• Fine-tune transformers and LLMs (LoRA/QLoRA) for entity extraction, classification and reranking.
• Extract entities from job descriptions and resumes, building prediction models, create knowledge graph and traversal models.
• Build retrieval, clustering and learning-to-rank pipelines over large taxonomies.
• Design evaluations, dig into errors, and ship models to production.

Skills

Python

PyTorch

Transformers

Hugging Face

Pandas

Scikit-learn

NLP

Data Analysis

Experimentation

Tools

Docker

AWS

SQL

Hugging Face

Job description

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.

About Prismforce and the AI & ML team

Prismforce is a Sequoia-backed vertical SaaS company building the Talent Supply Chain platform for global technology, engineering and IT services firms, with clients across IT services, banking, healthcare and automotive.

The AI & ML team builds the intelligence layer underneath every product. That layer powers SkillPrism, SelectPrism and IntelliPrism, and our AI-readiness assessments for enterprise workforces. You will work on models that run in production for some of the largest employers in India and the US.

What you'll do

1. Fine-tune transformers and LLMs (LoRA/QLoRA) for entity extraction, classification and reranking.

2. Extract entities from job descriptions and resumes, building prediction models, create knowledge graph and traversal models.

3. Build retrieval, clustering and learning-to-rank pipelines over large taxonomies.

4. Design evaluations, dig into errors, and ship models to production.

Requirements

1. Hands-on applied NLP or ML experience: 1-3 years for Data Scientist I, 3-5 years for Data Scientist II.

2. Strong Python, with PyTorch, Hugging Face Transformers, sentence-transformers, pandas and scikit-learn.

3. Has fine-tuned transformer models on their own data, such as an encoder for NER or classification, or a decoder LLM with PEFT (LoRA/QLoRA).

4. Solid grasp of embeddings, attention, contrastive training, and retrieval with bi-encoders and cross-encoders.

5. Working knowledge of classical ML: gradient boosting, logistic regression, clustering, and dimensionality reduction.

6. Comfort with statistics and evaluation design: sampling, significance, calibration and metric choice.

7. SQL, plus the ability to explore data at the scale of millions of records.

8. Clear written communication of experiments and results.

9. Experience with learning-to-rank, knowledge graphs or large taxonomies in a real product.

10. Experience with ML lifecycle tooling: experiment tracking, model versioning, Docker, and GPU training and serving on AWS.

Nice to have

• Graph databases and graph ML: Neo4j and Cypher, pgvector, node embeddings, link prediction.
• Span-based or zero-shot NER (GLiNER, NuNER) and entity linking to a fixed ontology.
• Distilling LLM outputs into small models, and efficient serving with vLLM, quantisation or ONNX.
• Managed LLM platforms such as AWS Bedrock or SageMaker, and structured-output prompting at scale.
• Agent and workflow orchestration (LangGraph, n8n or similar) for multi-step curation pipelines.
• Ontology or taxonomy design, or prior work in HR tech, talent or skills data.
• Open-source contributions, Kaggle results or publications in NLP or IR.
• Real scale: your models run on millions of enterprise data points.
• Breadth: you will touch extraction, graphs, ranking and LLMs, not one narrow slice.
• Ownership: a small, senior team where you ship and see the impact.
• Leadership from McKinsey, Deloitte, Amazon, Infosys, TCS and Uber, with an advisory board of former IT services CXOs.

Perks and benefits

• Group medical cover for self, spouse, children and parents
• Hybrid work and flexible policies
• A self-driven career progression framework