MLOps Data Scientist

Job Description:

We are seeking a highly skilled and motivated Data Scientist with 3-5 years of experience to join our team. The ideal candidate will have a strong background in Machine Learning (ML), Generative AI, Python, AWS, and SQL, with experience in developing and deploying ML models and AI Models. You will play a crucial role in analyzing complex data, building predictive models, and deploying scalable AI solutions.

Roles and Responsibilities:

• Design, develop, and deploy Machine Learning and Generative AI models for real-world applications.
• Work with large-scale structured and unstructured data, performing data preprocessing, feature engineering, and model optimization.
• Implement and optimize ML pipelines for training and inference in cloud environments, especially AWS (SageMaker, Lambda, S3, EC2, RDS, etc.).
• Develop end-to-end ML solutions, including model versioning, monitoring, and retraining.
• Collaborate with data engineers and software developers to integrate ML models into production systems.
• Perform exploratory data analysis (EDA) and statistical analysis to derive insights and drive decision-making.
• Write efficient SQL queries for data extraction, transformation, and analysis.
• Stay updated with the latest advancements in AI, ML, and Generative AI technologies, applying them to business use cases.
• Document processes, models, and findings effectively for both technical and non-technical stakeholders.

Required Skills & Qualifications:

• Proficiency in Python and ML libraries/frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).
• Hands-on experience with Generative AI models (e.g., GPT, DALL·E, Stable Diffusion, LLMs, etc.).
• Experience in developing, training, and deploying ML models on AWS (SageMaker, Lambda, S3, EC2).
• Strong knowledge of SQL for querying and managing large datasets.
• Understanding of MLOps principles, CI/CD pipelines for ML, and model monitoring.
• Familiarity with big data technologies (Spark, Hadoop) is a plus.
• Strong analytical skills and the ability to derive insights from complex datasets.
• Excellent communication skills with the ability to convey technical concepts to non-technical stakeholders.

Preferred Qualifications:

• Experience with cloud-based AI/ML services (Google Cloud AI, Azure ML, etc.).
• Knowledge of APIs, microservices, and containerization (Docker, Kubernetes).
• Experience with deep learning architectures (CNNs, RNNs, Transformers).
• Strong problem-solving skills and a results-driven approach.