Data Scientist-I/II

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.

Data Scientist I / II | Prismforce | Bangalore (Hybrid)

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.

Why join

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

Competitive compensation

Group medical cover for self, spouse, children and parents

Hybrid work and flexible policies

Retirement benefits

A self-driven career progression framework

Skills

LORAQLORAllmllm trainingFine tuning