Data Scientist Machine Learning & AI (GenAI Focus)

You are a highly skilled Data Scientist with expertise in Machine Learning, MLOps, and Generative AI. Your role involves designing, developing, and deploying machine learning models for real-world business problems. You will work on the end-to-end ML lifecycle, including data preprocessing, model building, evaluation, deployment, and monitoring. It is essential to implement and manage MLOps pipelines for scalable and reproducible workflows. You will utilize tools like MLflow for experiment tracking, model versioning, and lifecycle management. Additionally, you will develop and integrate Generative AI solutions such as LLM-based applications. Collaboration with cross-functional teams (engineering, product, business) to translate requirements into AI solutions is a key aspect of your responsibilities. It is crucial to optimize model performance, ensure production stability, and stay updated with the latest advancements in AI/ML and GenAI ecosystems. Key Responsibilities: - Design, develop, and deploy machine learning models for real-world business problems - Work on end-to-end ML lifecycle: data preprocessing, model building, evaluation, deployment, and monitoring - Implement and manage MLOps pipelines for scalable and reproducible workflows - Utilize tools like MLflow for experiment tracking, model versioning, and lifecycle management - Develop and integrate Generative AI (GenAI) solutions such as LLM-based applications - Collaborate with cross-functional teams (engineering, product, business) to translate requirements into AI solutions - Optimize model performance and ensure production stability - Stay updated with the latest advancements in AI/ML and GenAI ecosystems Required Skills & Qualifications: - 4+ years of experience in Data Science/Machine Learning - Strong programming skills in Python - Hands-on experience with ML modeling techniques (supervised, unsupervised, NLP, etc.) - Solid understanding of MLOps practices and tools - Experience with MLflow or similar model lifecycle tools - Practical experience in Generative AI (GenAI), including working with LLMs - Experience with libraries/frameworks like Scikit-learn, TensorFlow, PyTorch - Strong understanding of data structures, algorithms, and statistics - Experience with cloud platforms (AWS/GCP/Azure) is a plus Good to Have: - Experience with LLM fine-tuning, prompt engineering, or RAG pipelines - Exposure to Docker, Kubernetes, and CI/CD pipelines - Knowledge of data engineering workflows You are a highly skilled Data Scientist with expertise in Machine Learning, MLOps, and Generative AI. Your role involves designing, developing, and deploying machine learning models for real-world business problems. You will work on the end-to-end ML lifecycle, including data preprocessing, model building, evaluation, deployment, and monitoring. It is essential to implement and manage MLOps pipelines for scalable and reproducible workflows. You will utilize tools like MLflow for experiment tracking, model versioning, and lifecycle management. Additionally, you will develop and integrate Generative AI solutions such as LLM-based applications. Collaboration with cross-functional teams (engineering, product, business) to translate requirements into AI solutions is a key aspect of your responsibilities. It is crucial to optimize model performance, ensure production stability, and stay updated with the latest advancements in AI/ML and GenAI ecosystems. Key Responsibilities: - Design, develop, and deploy machine learning models for real-world business problems - Work on end-to-end ML lifecycle: data preprocessing, model building, evaluation, deployment, and monitoring - Implement and manage MLOps pipelines for scalable and reproducible workflows - Utilize tools like MLflow for experiment tracking, model versioning, and lifecycle management - Develop and integrate Generative AI (GenAI) solutions such as LLM-based applications - Collaborate with cross-functional teams (engineering, product, business) to translate requirements into AI solutions - Optimize model performance and ensure production stability - Stay updated with the latest advancements in AI/ML and GenAI ecosystems Required Skills & Qualifications: - 4+ years of experience in Data Science/Machine Learning - Strong programming skills in Python - Hands-on experience with ML modeling techniques (supervised, unsupervised, NLP, etc.) - Solid understanding of MLOps practices and tools - Experience with MLflow or similar model lifecycle tools - Practical experience in Generative AI (GenAI), including working with LLMs - Experience with libraries/frameworks like Scikit-learn, TensorFlow, PyTorch - Strong understanding of data structures, algorithms, and statistics - Experience with cloud platforms (AWS/GCP/Azure) is a plus Good to Have: - Experience with LLM fine-tuning, prompt engineering, or RAG pipelines - Exposure to Docker, Kubernetes, and CI/CD pipelines - Knowledge of data engineering workflows