AI Platform Engineer

Job Title: AI Platform Engineer Experience: 3–5 Years Location: USA – Remote

Key Responsibilities

Design and develop scalable platforms for AI and machine learning workloads. Build and maintain infrastructure for training, deploying, and serving AI models. Develop automated CI/CD pipelines for AI applications and machine learning models. Deploy and manage AI services using Docker, Kubernetes, and cloud-native technologies. Implement model-serving infrastructure for LLMs, generative AI, and traditional ML models. Integrate foundation models, embedding models, vector databases, and AI APIs into enterprise platforms. Build reusable APIs, SDKs, and platform components for AI development teams. Implement monitoring, logging, observability, and performance optimization for AI workloads. Manage GPU resources, distributed computing, and inference workloads. Implement access controls, secrets management, and security policies for AI platforms. Collaborate with data scientists, ML engineers, software engineers, and DevOps teams. Troubleshoot platform issues and ensure high availability, scalability, and reliability. Required Technical Skills Strong programming experience in Python. Experience with AWS, Microsoft Azure, or Google Cloud Platform. Hands-on experience with Docker and Kubernetes. Knowledge of CI/CD pipelines using Jenkins, GitHub Actions, or GitLab CI/CD. Understanding of MLOps principles and ML model deployment. Experience with REST APIs, microservices, and distributed systems. Familiarity with model-serving frameworks such as vLLM, NVIDIA Triton Inference Server, or TorchServe. Knowledge of infrastructure as code using Terraform or similar tools. Experience with monitoring tools such as Prometheus and Grafana. Understanding of authentication, authorization, API security, and secrets management. Education Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Software Engineering, or a related field. Equivalent practical experience may be considered. Candidate Requirements 3–5 years of experience in AI infrastructure, platform engineering, MLOps, or cloud engineering. Hands-on experience deploying production AI or ML services. Strong troubleshooting and problem-solving skills. Ability to work independently and collaborate across engineering teams. Excellent communication and documentation skills.