Key Responsibilities:

Develop and implement NLP-focused ML/DL solutions to power innovative, AI-driven products and features.
Build and optimize models for text classification, entity recognition, summarization, semantic search, and document understanding.
Work with traditional ML algorithms (Logistic Regression, SVM, Random Forest, XGBoost, etc.) and deep learning models (RNN, LSTM, GRU, Transformers).
Design and leverage embeddings for semantic similarity, clustering, and vector-based retrieval.
Explore and integrate Generative AI techniques into NLP applications like summarization, Q&A, and conversational systems.
Implement and optimize transformer architectures (BERT, RoBERTa, GPT, etc.) for real-world production workloads.
Collaborate with cross-functional teams to collect, clean, and preprocess unstructured textual data.
Deploy, monitor, and maintain models using MLOps best practices including containerized deployments (Docker, Kubernetes) and CI/CD pipelines.
Stay updated with cutting-edge research by reading research papers, blogs, and technical reports to bring the latest techniques into production.
Continuously enhance system performance and scalability by applying first-principles mathematical reasoning.

Qualifications:

Bachelors or Masters degree in Data Science, Computer Science, AI/ML, Statistics, or a related field.
3+ years of experience in Data science or machine learning roles with a strong focus on text-based solutions.

Technical Skills

Solid foundation in traditional ML algorithms and deep learning architectures.
Strong hands-on experience with sequence modeling techniques (RNN, LSTM, GRU) and state-of-the-art transformer architectures, including the BERT family and GPT-based models.
Strong knowledge of Data Structures and Operating System fundamentals
Good understanding of embeddings, semantic similarity techniques, and vector databases.
Knowledge of hyperparameter tuning strategies and model optimization techniques.
Strong grasp of mathematical fundamentals: Linear Algebra, Probability, Statistics, Optimization, and Calculus.
Proficiency in Python and frameworks like PyTorch, TensorFlow, or Keras.
Experience with SQL and preferably NoSQL databases.
Familiarity with MLOps concepts and tools for scalable deployment.
Awareness of Generative AI and its applications in NLP.

Preferred Skills:

Exposure to multi-agent orchestration frameworks (LangChain, LangGraph, MCP, etc.).
Experience with retrieval-augmented generation (RAG) and vector search pipelines.
Familiarity with containerized deployments using Docker and Kubernetes.
Working knowledge of cloud platforms (AWS, GCP, Azure).
Understanding of version control systems like Git.

Data Scientist