We are seeking an experienced Data Scientist with a strong focus on Natural Language Processing (NLP) and Large Language Models (LLMs) to join our innovative team. This role is designed for mid to senior-level professionals with at least 4 years of relevant experience in data science, machine learning, or related fields.

The ideal candidate will thrive in developing advanced AI solutions, fine-tuning LLMs, and applying data-driven insights to solve complex, real-world problems.

✨ Key Responsibilities

Design and implement NLP and LLM-based solutions (chatbots, sentiment analysis, information extraction, automated summarization).

Develop and optimize machine learning models, including deep learning architectures (CNNs, RNNs, LSTMs, Transformers).

Apply text preprocessing techniques (tokenization, embeddings, stemming, lemmatization).

Conduct fine-tuning and prompt engineering for LLMs, adapting models for business-specific use cases.

Build recommender systems and time-series forecasting models to drive business value.

Collaborate with cross-functional teams to integrate AI/ML solutions into production using MLOps practices.

Leverage cloud platforms (AWS, Azure, GCP) and ML services to scale and operationalize data science workflows.

Analyze large datasets to extract actionable insights and communicate findings effectively.

🔑 Requirements

Experience Level: Mid to Senior (4+ years in Data Science, NLP, or Machine Learning).

NLP & LLMs Expertise:

Strong knowledge of preprocessing (tokenization, embeddings, stemming/lemmatization).

Experience with classical models (TF-IDF, Word2Vec, GloVe) and transformer-based models (BERT, GPT, RoBERTa).

Hands-on experience with fine-tuning and prompt engineering.

AI Knowledge:

Proficiency with deep learning frameworks (CNNs, RNNs, LSTMs, Transformers).

Familiarity with multimodal models (e.g., CLIP).

Experience with recommender systems and time-series forecasting.

Reinforcement learning is a plus.

Technical Stack:

Must-have: Python (Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow).

Must-have: Cloud platforms (AWS, Azure, GCP) and ML services (SageMaker, Vertex AI, Azure ML).

Familiarity with MLOps tools: Docker, Kubernetes, MLflow, DVC, CI/CD.

Language Proficiency: English C1 level preferred; B2 considered for technically strong candidates.

Education: Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Engineering, or equivalent proven experience.