Junior AI Engineer

Role Overview: As an AI Engineer, you will lead the design, development, and implementation of artificial intelligence solutions to address specific business problems. Your role will involve collaborating closely with data scientists, data engineers, and cloud engineers to deliver AI solutions that drive business value. You should have extensive experience in machine learning models, natural language processing, computer vision, or other AI technologies. Key Responsibilities: - AI Solution Development: - Design and develop machine learning models, algorithms, and AI systems. - Implement and optimize AI models for performance and scalability. - Data Management and Preprocessing: - Collect, preprocess, and analyze large datasets to build effective AI models. - Ensure data quality and integrity through rigorous data validation processes. - Model Training and Evaluation: - Train, test, and validate AI models to ensure accuracy and reliability. - Perform model evaluation and tuning to improve performance metrics. - Integration and Deployment: - Integrate AI models into existing systems and applications. - Deploy AI solutions into production environments and monitor their performance. - Collaboration and Communication: - Work closely with data scientists, software engineers, and business stakeholders to understand requirements and deliver solutions. - Communicate complex AI concepts and results to non-technical stakeholders. - Research and Innovation: - Stay updated with the latest AI trends, technologies, and best practices. - Conduct research to explore new AI methodologies and applications. - Documentation and Reporting: - Document AI models, processes, and workflows. - Prepare reports and presentations on AI project status, results, and impacts. Qualifications Required: - Educational Background: - Bachelors degree in Computer Science, Data Science, or a related field is required. A Masters degree is a plus. - Experience: - 0-2 years of experience in AI roles with the required skills. - Deep Learning & Transformer Architecture: - Basic understanding of neural networks and their applications. - Familiarity with transformer architecture and its use cases. - Exposure to multi-modal diffusion architectures is a good-to-have. - LLM Development & Implementation: - Hands-on experience in building and fine-tuning large language models (LLMs) for various applications. - Experience with embedding models and vector database integration. - Multi-Agent & Rules-Based AI Implementation: - Ability to implement rules-based AI logic in applications. - Experience in developing multi-agent AI systems with orchestration. - AI Tools & Libraries: - Strong knowledge of AWS AI services. - Hands-on experience with Hugging Face, OpenAI API, and GPT models. - Cloud & DevOps (AWS Focused): - Experience in deploying AI models using AWS services. - Familiarity with implementing CI/CD pipelines for AI models using AWS services. - Model Deployment & API Integration: - Hands-on Python experience with at least one AI project delivered from inception to production. - Experience in API development and integration, including REST APIs, GraphQL, and AWS API Gateway. (Note: Preferred qualifications and additional company details section are omitted as per the given instructions) Role Overview: As an AI Engineer, you will lead the design, development, and implementation of artificial intelligence solutions to address specific business problems. Your role will involve collaborating closely with data scientists, data engineers, and cloud engineers to deliver AI solutions that drive business value. You should have extensive experience in machine learning models, natural language processing, computer vision, or other AI technologies. Key Responsibilities: - AI Solution Development: - Design and develop machine learning models, algorithms, and AI systems. - Implement and optimize AI models for performance and scalability. - Data Management and Preprocessing: - Collect, preprocess, and analyze large datasets to build effective AI models. - Ensure data quality and integrity through rigorous data validation processes. - Model Training and Evaluation: - Train, test, and validate AI models to ensure accuracy and reliability. - Perform model evaluation and tuning to improve performance metrics. - Integration and Deployment: - Integrate AI models into existing systems and applications. - Deploy AI solutions into production environments and monitor their performance. - Collaboration and Communication: - Work closely with data scientists, software engineers, and business stakeholders to understand requirements and deliver solutions. - Communicate complex AI concepts and results to non-technical stakeholders. - Research and Innovation: - Stay updated with the latest AI trends, technologies, and best practices. - Conduct research to explore new AI methodologies and applications.