Data Scientist

Key Responsibilities:

• Cloud Solutions Design & Implementation: Architect deploy and maintain robust scalable and secure cloud solutions on platforms such as AWS and Azure.
• MLOps & Automation: Build and optimize MLOps pipelines on cloud platforms ensuring efficient deployment monitoring and scaling of machine learning models.
• CI/CD Pipeline Orchestration: Implement CI/CD pipelines using tools like GitLab CI GitHub Actions and Jenkins to automate and streamline workflows across data science projects.
• Data Pipeline & Engineering Infrastructure: Design develop and manage data pipelines and infrastructure to support largescale enterprise machine learning systems ensuring high availability and lowlatency processing.
• Infrastructure Management: Oversee network operations and infrastructure management for machine learning and AI systems ensuring optimized performance security and reliability.
• Collaboration with DevOps: Collaborate with DevOps teams to manage containerization (Docker Kubernetes) and infrastructure as code (Terraform CloudFormation) for ML model deployment and monitoring.
• Deep Learning & NLP: Apply cuttingedge Deep Learning and Natural Language Processing techniques to solve complex problems particularly in areas like text analysis speech recognition and language generation.
• Large Language Model (LLM) Infrastructure: Design and maintain infrastructure supporting Large Language Models (LLMs) enabling scalable deployment optimization and realtime inference.
• Continuous Learning & Development: Stay updated with the latest advancements in data science cloud infrastructure and MLOps to drive innovation within the organization.

Skills & Qualifications:

• 34 years of experience in Data Science Machine Learning or related fields with expertise in cloud infrastructure and MLOps.
• Handson experience with cloud platforms such as AWS Azure or GCP with strong knowledge of cloudnative services for machine learning and AI.
• Expertise in MLOps tools like MLflow Kubeflow or SageMaker.
• Strong proficiency in CI/CD pipelines using tools such as GitLab CI GitHub Actions or Jenkins.
• Deep knowledge of DevOps practices and tools including Docker Kubernetes Terraform and Ansible.
• Experience with deep learning frameworks such as TensorFlow PyTorch or Keras.
• Handson experience with Natural Language Processing (NLP) libraries and techniques.
• Strong programming skills in Python R or similar languages.
• Proven ability to design deploy and manage largescale LLM infrastructure.
• Understanding of network operations and infrastructure management for cloud environments.
• Strong analytical and problemsolving skills with a focus on delivering scalable solutions.

machine learning,data science,cloud,pipelines,infrastructure