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AI Engineer - Production AI / MLOps
InterSources
3 hours ago
Full-time
On-site
Philadelphia, Pennsylvania, United States
AI Engineer – Production AI / MLOps
We are looking for an AI Engineer with strong experience in production deployment of AI systems, CI/CD automation, and scalable AI engineering practices. Candidates should have hands-on experience operationalizing machine learning and generative AI applications in enterprise environments. Responsibilities: Build and deploy AI/ML applications into production environments. Design and maintain CI/CD pipelines for AI/ML workloads. Manage model lifecycle, testing, monitoring, and deployment automation. Work with cloud-native AI infrastructure on AWS. Collaborate with engineering teams to productionize AI solutions. Implement MLOps best practices and model governance. Required Skills: 4–5 years of engineering experience. Strong Python development experience. Hands-on experience with AI/ML production deployment. Experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI, Azure DevOps). Experience with Docker, Kubernetes, and containerization. Strong AWS cloud experience. Experience with monitoring and observability of AI systems. Preferred Skills: Experience with LLMs, RAG, or GenAI systems. Exposure to MLflow, Kubeflow, SageMaker, or similar tools. Financial domain experience.
We are looking for an AI Engineer with strong experience in production deployment of AI systems, CI/CD automation, and scalable AI engineering practices. Candidates should have hands-on experience operationalizing machine learning and generative AI applications in enterprise environments. Responsibilities: Build and deploy AI/ML applications into production environments. Design and maintain CI/CD pipelines for AI/ML workloads. Manage model lifecycle, testing, monitoring, and deployment automation. Work with cloud-native AI infrastructure on AWS. Collaborate with engineering teams to productionize AI solutions. Implement MLOps best practices and model governance. Required Skills: 4–5 years of engineering experience. Strong Python development experience. Hands-on experience with AI/ML production deployment. Experience with CI/CD tools (Jenkins, GitHub Actions, GitLab CI, Azure DevOps). Experience with Docker, Kubernetes, and containerization. Strong AWS cloud experience. Experience with monitoring and observability of AI systems. Preferred Skills: Experience with LLMs, RAG, or GenAI systems. Exposure to MLflow, Kubeflow, SageMaker, or similar tools. Financial domain experience.