Senior AI Engineer - Production GenAI and Machine Learning Systems

Job Overview

Join the AI Centre of Excellence as a Senior AI Engineer, focusing on the creation and scaling of robust AI/ML, Generative AI, Simulation, and Search systems designed for production environments. This role serves as a crucial link between data science and engineering, ensuring deployment, monitoring, and maintenance of AI models with high reliability in live settings.

Core Responsibilities

• Architect and develop scalable machine learning pipelines suitable for both batch processing and real-time operations.
• Construct model serving systems using APIs and microservices to support production demands.
• Enhance model inference efficiency and reduce latency.
• Establish CI/CD pipelines tailored for ML workflows to automate testing and deployment.
• Oversee model lifecycle management including version control, monitoring, and retraining mechanisms.
• Guarantee reproducibility and consistent performance of deployed ML solutions.
• Integrate ML applications with enterprise data platforms and collaborate across AI CoE teams to leverage platform capabilities.
• Develop self-service deployment tools to streamline model deployment for AI teams, promoting rapid experimentation and efficient rollout.
• Lead GenAI platform initiatives by building retrieval-augmented generation pipelines, large language model orchestration frameworks, and reusable GenAI APIs and services.
• Support multi-agent and autonomous workflows within GenAI solutions.
• Implement and maintain scalable serving infrastructures utilizing technologies such as vLLM and Triton, including management of vector databases and embedding pipelines.
• Optimize both system performance and cost-efficiency for large language model operations.
• Continuously evaluate emerging Generative AI technologies and promote the adoption of industry-leading practices in LLM operations.
• Collaborate closely with data scientists to transition models to production and partner with platform teams to ensure scalability and optimal system performance.

Required Qualifications and Experience

• Completion of Bachelor’s, Master’s, or Doctorate degree in Computer Science, Mathematics, or equivalent field.
• A demonstrated ongoing dedication to professional growth in areas including AI/ML, Generative AI, aviation industry knowledge particularly in cargo handling, ground handling, freight, and food logistics solutions.
• Extensive professional experience totaling between 6 to 10 years in machine learning engineering or software engineering roles.
• Strong proficiency with Python programming and FastAPI framework development.
• Advanced experience working with containerization and orchestration technologies such as Docker and Kubernetes.
• Expertise in ML lifecycle tools such as MLflow, Kubeflow, along with expertise in setting up and maintaining continuous integration and continuous deployment pipelines.