Sr Associate - AI | ML Engineer

As an AI Platform Lead Engineer, your role involves building and maintaining systems that support AI and machine learning applications. You will focus on ensuring the systems are scalable, efficient, and reliable. Key Responsibilities and Duties: - Deploy, manage, and scale AI systems in cloud environments - Support model activity and deployment infrastructure - Collaborate with infrastructure and application development teams, data engineers, and model owners for better integration of AI applications - Put ML models into production with considerations for scalability, optimization, resource availability, and security Educational Requirements: - University degree preferred Work Experience: - Required: 5+ years - Preferred: 7+ years Physical Requirements: - Sedentary Work Generative AI Development: - Design and implement Generative AI solutions, including: - RAG (Retrieval-Augmented Generation) pipelines: Build systems integrating vector databases, embedding models, and LLMs - Prompt engineering and optimization: Develop robust prompting strategies, templates, and workflows - LLM fine-tuning and reinforcement learning: Customize foundation models using various methods to improve performance - Model evaluation and benchmarking: Establish evaluation frameworks and conduct testing across models - Observability and monitoring: Implement logging, tracing, and alerting systems for production environments - Build production-grade AI agents using low-code platforms and high-code custom implementations - Integrate diverse AI/ML capabilities to solve complex business problems - Stay current with the evolving AI landscape to enhance solution capabilities (Note: The additional details of the company were not present in the provided job description) As an AI Platform Lead Engineer, your role involves building and maintaining systems that support AI and machine learning applications. You will focus on ensuring the systems are scalable, efficient, and reliable. Key Responsibilities and Duties: - Deploy, manage, and scale AI systems in cloud environments - Support model activity and deployment infrastructure - Collaborate with infrastructure and application development teams, data engineers, and model owners for better integration of AI applications - Put ML models into production with considerations for scalability, optimization, resource availability, and security Educational Requirements: - University degree preferred Work Experience: - Required: 5+ years - Preferred: 7+ years Physical Requirements: - Sedentary Work Generative AI Development: - Design and implement Generative AI solutions, including: - RAG (Retrieval-Augmented Generation) pipelines: Build systems integrating vector databases, embedding models, and LLMs - Prompt engineering and optimization: Develop robust prompting strategies, templates, and workflows - LLM fine-tuning and reinforcement learning: Customize foundation models using various methods to improve performance - Model evaluation and benchmarking: Establish evaluation frameworks and conduct testing across models - Observability and monitoring: Implement logging, tracing, and alerting systems for production environments - Build production-grade AI agents using low-code platforms and high-code custom implementations - Integrate diverse AI/ML capabilities to solve complex business problems - Stay current with the evolving AI landscape to enhance solution capabilities (Note: The additional details of the company were not present in the provided job description)