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We are seeking a highly skilled and experienced Senior AI Engineer to lead the design, development, and implementation of robust and scalable pipelines and backend systems for our Generative AI applications. In this role, you will be responsible for orchestrating the flow of data, integrating AI services, developing RAG pipelines, working with LLMs, and ensuring the smooth operation of the backend infrastructure that powers our Generative AI solutions.
Job Responsibility
Design and implement efficient and scalable pipelines for data ingestion, processing, and transformation, tailored for Generative AI workloads
Orchestrate the flow of data between various AI services, databases, and backend systems within the Generative AI context
Build and maintain CI/CD pipelines for deploying and updating Generative AI services and pipelines
Develop and manage systems for ingesting diverse data sources (text, images, code, etc.) used in Generative AI applications
Implement OCR and other preprocessing techniques to prepare data for use in Generative AI pipelines
Ensure data quality, consistency, and security throughout the ingestion process
Integrate and manage external AI services (e.g., cloud-based APIs for image generation, text generation, LLMs) into our Generative AI applications
Develop and maintain APIs for seamless communication between AI services and backend systems
Monitor and optimize the performance of integrated AI services within the Generative AI pipeline
Design and implement RAG pipelines to enhance Generative AI capabilities with external knowledge sources
Develop and optimize data retrieval and indexing strategies for RAG systems used in conjunction with Generative AI
Evaluate and improve the accuracy and relevance of RAG-generated responses in the context of Generative AI applications
Develop and manage interactions with LLMs through APIs and SDKs within Generative AI pipelines
Implement prompt engineering strategies to optimize LLM performance for specific Generative AI tasks
Analyze and debug LLM outputs to ensure quality and consistency in Generative AI applications
Design, develop, and maintain backend services that support Generative AI applications
Ensure the scalability, reliability, and security of backend infrastructure for Generative AI workloads
Implement monitoring and logging systems for backend services and pipelines supporting Generative AI
Troubleshoot and resolve backend-related issues impacting Generative AI applications
Requirements
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field
5+ years of experience in AI/ML development with a focus on building and deploying AI pipelines and backend systems
Proven experience in designing and implementing data ingestion and processing pipelines
Strong experience with cloud platforms (e.g., AWS, Azure, GCP) and their AI/ML services
Expertise in Python and relevant AI/ML libraries
Strong understanding of AI infrastructure and deployment strategies
Experience with data engineering and data processing techniques
Proficiency in software development principles and best practices
Experience with containerization and orchestration tools (e.g., Docker, Kubernetes)
Experience with version control (Git)
Experience with RESTful APIs and API development
Experience with vector databases and their application in AI/ML, particularly for similarity search and retrieval
Familiarity with Generative AI concepts and techniques (e.g., GANs, Diffusion Models, VAEs, LLMs)
Experience with integrating and managing Generative AI services
Understanding of RAG pipelines and their application in Generative AI
Experience with prompt engineering for LLMs in Generative AI contexts
Strong problem-solving and analytical skills
Excellent communication and collaboration skills
Ability to work in a fast-paced environment
Nice to have
Experience with OCR and document processing technologies
Experience with MLOps practices for Generative AI
Contributions to open-source AI projects
Strong experience with vector databases and their optimization for Generative AI applications