Job Title        : Programmer Dev - India
Experience    : 0-5 Years
Location        : Bengaluru

Description : 

• Responsibilities Solution Design and Architecture Design and architect end-to-end data science solutions that align with business objectives and technical requirements.
• Develop scalable and maintainable data science workflows, including data ingestion, preprocessing, modeling, and deployment. Ensure the integration of data science solutions with existing systems and platforms.
• Technical Leadership Provide technical leadership and guidance to data scientists, data engineers, and other stakeholders. Stay updated with the latest advancements in data science, machine learning, and AI technologies, and apply them to improve solution designs.
• Project Management Lead the planning, execution, and delivery of data science projects, ensuring they are completed on time and within budget. Collaborate with project managers to define project scope, milestones, and deliverables. Monitor project progress and provide regular updates to stakeholders.
• Data Strategy and Governance Define data strategy and governance frameworks to ensure data quality, security, and compliance.
• Establish best practices for data management, including data acquisition, storage, and processing. Collaboration and Communication Work closely with business stakeholders to understand their needs and translate them into technical requirements. Communicate complex technical concepts to non-technical stakeholders in a clear and concise manner.
• Foster a collaborative environment to facilitate knowledge sharing and innovation. Solution Implementation and Deployment Oversee the implementation of data science solutions, including the development and deployment of machine learning models. Ensure solutions are robust, scalable, and perform well in production environments.
• Conduct code reviews and ensure adherence to coding standards and best practices. Performance Optimization and Troubleshooting Optimize the performance of data science solutions, including model accuracy, computational efficiency, and resource utilization. Improve efficiency by creating repeatable and reusable modules
• Troubleshoot and resolve technical issues related to data science solutions. Qualifications Technical Skills Strong programming skills in languages such as Python, R, or Scala. Expertise in machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn). Proficiency in data manipulation and analysis using SQL and data processing tools (e.g., Apache Spark, Hadoop).
• Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and containerization technologies (e.g., Docker, Kubernetes). Technical Skills Programming Languages Strong programming skills in languages such as Python, R, or Scala. Machine Learning Frameworks and Libraries Expertise in machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, scikit-learn). Generative AI (GenAI) Experience with Generative AI models (e.g., GPT, BERT, DALL-E) and frameworks (e.g., Hugging Face Transformers, OpenAI GPT-3). Knowledge of fine-tuning GenAI models for specific tasks and industries. Ability to design and implement GenAI solutions for various applications such as text generation, image generation, and conversational AI. Familiarity with techniques for training and deploying
• GenAI models. Experience in leveraging GenAI for tasks such as automated content creation and data augmentation. MLOps Proficiency in MLOps practices, including model deployment, monitoring, and continuous integration/continuous deployment (CI/CD) for machine learning models. Experience with MLOps tools and platforms (e.g., MLflow, Kubeflow, TFX). Data Manipulation and Analysis Proficiency in data manipulation and analysis using SQL and data processing tools (e.g., Apache Spark, Hadoop).
• Cloud Platforms Experience with cloud platforms (e.g., AWS, Azure, Google Cloud) and their data science and machine learning services. Understanding of cloud infrastructure and services for scalable AI deployments. Containerization and Orchestration Proficiency in containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes) for deploying and managing data science solutions. Big Data Technologies Experience with big data technologies (e.g., Apache Kafka, Apache Flink) for handling and processing large datasets.