Artificial Intelligence Engineer
Smart IT Frame LLC
We are looking for an experienced AI Platform Engineer to design, build, and maintain scalable infrastructure and platforms that support AI/ML workloads, Generative AI applications, and agentic AI solutions. The ideal candidate will have strong experience in cloud infrastructure, Kubernetes, MLOps/LLMOps, automation, and deploying AI models and services into production environments.
Key Responsibilities
Design, build, and maintain scalable AI platforms supporting machine learning, Generative AI, and LLM-based applications.
Develop and manage cloud-native infrastructure using AWS, Azure, or GCP.
Deploy, manage, and scale AI workloads using Docker, Kubernetes, and container orchestration technologies.
Build and maintain CI/CD pipelines for AI model deployment, application releases, and infrastructure automation.
Implement MLOps and LLMOps practices for model lifecycle management, versioning, deployment, monitoring, and continuous improvement.
Support the deployment and integration of LLMs, RAG pipelines, vector databases, and AI agent frameworks.
Automate infrastructure provisioning and configuration management using Terraform, Ansible, or similar tools.
Establish monitoring, logging, observability, security, and governance standards for AI platforms.
Optimize platform performance, scalability, reliability, and cloud infrastructure costs.
Collaborate with data scientists, ML engineers, AI developers, and DevOps teams to deliver production-ready AI solutions.
Implement access controls, secrets management, data protection, and responsible AI platform practices.
Required Skills
Strong experience in platform engineering, DevOps, cloud engineering, or ML infrastructure.
Hands-on experience with AWS, Azure, or GCP.
Proficiency in Kubernetes, Docker, and containerized application deployment.
Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps.
Infrastructure as Code experience using Terraform, CloudFormation, or equivalent tools.
Knowledge of Python and scripting for automation.
Experience with MLOps/LLMOps tools and AI model deployment workflows.
Understanding of LLMs, Generative AI, RAG architectures, embeddings, and vector databases.
Experience with monitoring and observability tools such as Prometheus, Grafana, ELK, or cloud-native monitoring services.
Strong understanding of networking, IAM, security, and production system reliability.
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Key Responsibilities
Design, build, and maintain scalable AI platforms supporting machine learning, Generative AI, and LLM-based applications.
Develop and manage cloud-native infrastructure using AWS, Azure, or GCP.
Deploy, manage, and scale AI workloads using Docker, Kubernetes, and container orchestration technologies.
Build and maintain CI/CD pipelines for AI model deployment, application releases, and infrastructure automation.
Implement MLOps and LLMOps practices for model lifecycle management, versioning, deployment, monitoring, and continuous improvement.
Support the deployment and integration of LLMs, RAG pipelines, vector databases, and AI agent frameworks.
Automate infrastructure provisioning and configuration management using Terraform, Ansible, or similar tools.
Establish monitoring, logging, observability, security, and governance standards for AI platforms.
Optimize platform performance, scalability, reliability, and cloud infrastructure costs.
Collaborate with data scientists, ML engineers, AI developers, and DevOps teams to deliver production-ready AI solutions.
Implement access controls, secrets management, data protection, and responsible AI platform practices.
Required Skills
Strong experience in platform engineering, DevOps, cloud engineering, or ML infrastructure.
Hands-on experience with AWS, Azure, or GCP.
Proficiency in Kubernetes, Docker, and containerized application deployment.
Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI/CD, or Azure DevOps.
Infrastructure as Code experience using Terraform, CloudFormation, or equivalent tools.
Knowledge of Python and scripting for automation.
Experience with MLOps/LLMOps tools and AI model deployment workflows.
Understanding of LLMs, Generative AI, RAG architectures, embeddings, and vector databases.
Experience with monitoring and observability tools such as Prometheus, Grafana, ELK, or cloud-native monitoring services.
Strong understanding of networking, IAM, security, and production system reliability.
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Reference: WJ-3875_14395099