Data Scientist - Machine Learning/Artificial Intelligence

We are seeking a Python Developer with strong AI/ML experience to design, develop, and deploy machine learning models and intelligent systems. The ideal candidate has hands-on experience with modern ML frameworks, data pipelines, and production-grade Python applications.

Key Responsibilities :

Machine Learning & AI Development :

- Build, train, and optimize ML models for classification, prediction, NLP, computer vision, or recommendation systems.

- Implement deep learning architectures using frameworks such as TensorFlow, PyTorch, Keras, or JAX.

- Research and prototype models using state-of-the-art algorithms and techniques.

Python Engineering :

- Write clean, reusable, and efficient Python code.

- Develop scalable backend pipelines, data preprocessing workflows, and automation tools.

- Integrate ML models into production environments (REST APIs, microservices, cloud functions).

Data Engineering & Pipelines :

- Work with large datasets: cleaning, transformation, feature engineering.

- Build data pipelines using Pandas, NumPy, Spark, Airflow, etc.

- Work with databases (SQL/NoSQL) and cloud storage.

Deployment & MLOps :

- Deploy models using Docker, Kubernetes, FastAPI/Flask, or serverless environments.

- Monitor and maintain model performance in production.

- Implement MLOps best practices: versioning, CI/CD, model registry, automated training pipelines.

Collaboration & Documentation :

- Work closely with data scientists, engineers, and product teams.

- Document solutions, technical decisions, and experiment results.

Required Skills :

- Strong proficiency in Python.

- Hands-on experience in ML frameworks: TensorFlow, PyTorch, Scikit-learn.

- Solid understanding of ML concepts: supervised/unsupervised learning, evaluation metrics, optimization.

- Experience with REST APIs, data pipelines, and cloud platforms (AWS/GCP/Azure).

- Familiarity with Git, CI/CD, and containerization (Docker).

Preferred / Nice-to-Have :

- Experience with NLP (Transformers, LLMs) or computer vision (OpenCV).

- Knowledge of distributed training and GPU acceleration (CUDA).

- Experience with MLOps tools: MLflow, Kubeflow, Weights & Biases.

- Background in mathematics, statistics, or deep learning research.

- Exposure to big data technologies (Spark, Hadoop).

Education & Experience :

- Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or related fields.

- 2-7 years of relevant experience (depending on role level).