๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ฎ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ณ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ฎ๐ฌ-๐ณ๐ฌ ๐๐ฃ๐)
Experience: 1+ yrs
Location: Bangalore
We are looking for talentedย Data Scientists (Level I / II)ย to build and deploy production-grade machine learning, natural language processing (NLP), and large language model (LLM) solutions for enterprise talent and workforce intelligence platforms.
The ideal candidates will have strong Python programming skills, hands-on experience with applied NLP or machine learning, and practical knowledge of transformer models, information retrieval, and model evaluation. This role offers the opportunity to work on large-scale AI systems that extract insights from resumes, job descriptions, skills taxonomies, and other enterprise datasets.
Requirements
Key Responsibilities
- Fine-tune transformer models and LLMs using techniques such asย LoRA and QLoRAย for entity extraction, classification, and reranking.
- Develop NLP pipelines to extract structured entities, skills, and relevant information from resumes, job descriptions, and other unstructured datasets.
- Build predictive models for entity classification, entity linking, and skills intelligence.
- Design and implement knowledge graphs, graph traversal methods, and relationships across large skills taxonomies.
- Develop retrieval, clustering, semantic search, and learning-to-rank pipelines using embeddings, bi-encoders, and cross-encoders.
- Apply classical machine learning techniques, including gradient boosting, logistic regression, clustering, and dimensionality reduction.
- Design robust evaluation frameworks, analyse model errors, select appropriate metrics, and validate improvements through statistical analysis.
- Process and analyse large datasets containing millions of records using Python, SQL, and relevant data processing tools.
- Deploy and optimise machine learning models for production environments, ensuring scalability, reliability, and performance.
- Use experiment tracking, model versioning, Docker, and AWS-based GPU training and serving workflows.
- Collaborate with engineering and product teams to integrate AI capabilities into enterprise applications.
- Document experiments, communicate findings, and continuously improve model quality, efficiency, and business impact.
What Makes You a Great Fit
- 1โ3 years of experience for Data Scientist I or 3โ5 years for Data Scientist IIย in applied NLP, machine learning, or a related field.
- Strong proficiency in Python and practical experience with PyTorch, Hugging Face Transformers, sentence-transformers, pandas, and scikit-learn.
- Hands-on experience fine-tuning transformer models on custom datasets for named entity recognition (NER), classification, or LLM-based tasks using PEFT techniques such as LoRA/QLoRA.
- Strong understanding of embeddings, attention mechanisms, contrastive learning, semantic retrieval, bi-encoders, and cross-encoders.
- Working knowledge of classical machine learning algorithms and statistical evaluation methods.
- Experience with SQL and analysing large-scale datasets.
- Understanding of learning-to-rank systems, knowledge graphs, entity linking, or large taxonomies.
- Familiarity with ML lifecycle practices, including experiment tracking, model versioning, Docker, and cloud-based model deployment.
- Strong analytical thinking, problem-solving ability, and attention to model quality.
- Clear written communication skills and the ability to explain experiments, results, and technical trade-offs.