๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ฏ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ฑ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ฏ๐ฌ-๐ฑ๐ฌ ๐๐ฃ๐)
Experience: 1+ yrs
Location: Bangalore
We are looking for a talented and technically strongย Data Scientist I / IIย to develop and deploy production-grade machine learning, Natural Language Processing (NLP), and Large Language Model (LLM) solutions. The role focuses on building intelligent systems for entity extraction, classification, semantic retrieval, ranking, and knowledge graph applications using large-scale enterprise datasets.
The ideal candidate will have hands-on experience with Python, transformer models, applied NLP, and machine learning, along with a strong understanding of model evaluation and production deployment. This is an opportunity to work on complex AI challenges involving millions of records while building scalable solutions that deliver measurable business value.
Requirements
Key Responsibilities
- Fine-tune transformer models and LLMs using techniques such asย LoRA and QLoRAย for entity extraction, classification, and reranking.
- Develop NLP models to extract, classify, and link entities from resumes, job descriptions, and other unstructured enterprise data.
- Build prediction models, knowledge graphs, and graph traversal solutions to represent relationships between skills, roles, and competencies.
- Design and implement semantic retrieval, clustering, and learning-to-rank pipelines across large taxonomies and datasets.
- Develop embedding-based search solutions using bi-encoders, cross-encoders, and related retrieval techniques.
- Apply classical machine learning methods, including gradient boosting, logistic regression, clustering, and dimensionality reduction.
- Design evaluation frameworks, select appropriate metrics, analyse model errors, and validate performance through systematic experiments.
- Explore datasets containing millions of records using Python, SQL, and data-processing tools.
- Deploy, monitor, and improve machine learning models in production environments.
- Use experiment tracking, model versioning, Docker, and cloud infrastructure to support reproducible ML workflows.
- Collaborate with product, data, and engineering teams to translate business problems into effective AI solutions.
- Document experiments, communicate findings, and continuously improve model accuracy, efficiency, and reliability.
What Makes You a Great Fit
- 1+ years of relevant experienceย in applied machine learning, NLP, or data science; experience expectations may vary between Data Scientist I and II.
- Strong proficiency inย Python, with hands-on experience using PyTorch, Hugging Face Transformers, sentence-transformers, pandas, and scikit-learn.
- Practical experience fine-tuning transformer models on custom datasets for named entity recognition, classification, or related NLP tasks.
- Experience with LLM fine-tuning and parameter-efficient techniques such asย PEFT, LoRA, or QLoRA.
- Strong understanding of embeddings, attention mechanisms, contrastive learning, semantic search, and information retrieval.
- Working knowledge of classical machine learning algorithms and statistical evaluation methods.
- Experience with SQL and the ability to analyse large-scale datasets.
- Understanding of model evaluation, sampling, statistical significance, calibration, and metric selection.
- Familiarity with ML deployment practices, experiment tracking, model versioning, Docker, and GPU-based training or serving on AWS.
- Experience with learning-to-rank, knowledge graphs, entity linking, or large-scale taxonomies is an advantage.
- Familiarity with Neo4j, Cypher, pgvector, graph ML, GLiNER, NuNER, vLLM, ONNX, AWS Bedrock, or SageMaker is a plus.
- Strong analytical thinking, problem-solving, and written communication skills.
- Ability to work collaboratively, take ownership of experiments, and translate research into production-ready solutions.
- A strong interest in applied AI, modern NLP, LLM systems, and continuous experimentation.