ai software engineer.

• Build and deploy AI/ML solutions (including LLMs): Translate models into scalable, production-ready software and services.

• Develop and maintain infrastructure for LLMs: Design pipelines for data ingestion, preprocessing, model training (including fine-tuning), deployment, and monitoring.

• Optimize AI models for performance and efficiency: Address speed, scalability, and resource constraints, especially for LLM inference.

• Integrate AI models into systems: Build and manage APIs to deliver AI and LLM capabilities across products.

• Implement MLOps best practices: Manage CI/CD pipelines, testing, deployment, and monitoring, tailored for both ML and LLM workflows.

• Monitor production systems: Identify and resolve performance issues, with a focus on LLM stability and behavior.

• Collaborate across teams: Provide engineering support throughout the model development cycle, especially on LLM deployment feasibility.

• Stay ahead of the curve: Keep up with the latest in AI/ML and LLM research and tools.

• Document systems and architectures: Clearly capture designs, workflows, and deployment strategies, especially for LLM integrations.

• Maintain code quality: Follow software engineering best practices with attention to maintainability and testing of LLM-based systems.

about the manager/team
This role reports for Application director AI/ML

skills and experience required

• 3+ years of experience as a Software Engineer with a demonstrable focus on AI/ML projects.
• Strong proficiency in Python and experience with relevant AI/ML libraries and frameworks such as TensorFlow, PyTorch, scikit-learn, Pandas, and NumPy.
• Experience in deploying and scaling machine learning models, including Large Language Models, in a production environment.
• Solid understanding of cloud computing platforms (e.g., AWS, Azure, GCP) and their AI/ML services, including services relevant to LLM deployment (e.g., managed inference endpoints).
• Hands-on experience with containerization technologies (e.g., Docker, Kubernetes).
• Familiarity with CI/CD pipelines and MLOps principles, with specific understanding of how they apply to LLMs.
• Experience with API development and integration, including building APIs for interacting with LLMs.
• Strong understanding of software development principles, data structures, and algorithms.
• Excellent problem-solving, analytical, and debugging skills, including the ability to troubleshoot issues specific to LLM behavior.
• Strong communication and collaboration skills, with the ability to work effectively in a team environment, including discussing the nuances of LLM capabilities and limitations.
• Have experience working with Large Language Models (LLMs) and Transformer architectures (e.g., GPT, BERT, Llama, DeepSeek). This includes practical experience in prompt engineering, fine-tuning, evaluation, and deployment of LLMs.

To apply online please use the 'apply' function,
(EA: 94C3609/ R1324990 )