AI Operations Platform Consultant Jobs (Hybrid work)

Pursue a career at the cutting edge of enterprise artificial intelligence by exploring AI Operations Platform Consultant jobs. This specialized role sits at the critical intersection of data science, software engineering, and IT operations, focusing on the deployment, scaling, and maintenance of AI systems in real-world production environments. Professionals in this field are the essential architects and custodians of the infrastructure that allows machine learning (ML) and large language models (LLMs) to deliver reliable business value. They transform experimental models into robust, scalable, and efficient services that power applications and decision-making processes. An AI Operations Platform Consultant is primarily responsible for the entire lifecycle of AI models in production, a discipline often referred to as MLOps or LLMOps. Typical day-to-day duties involve designing and implementing pipelines for continuous integration and delivery (CI/CD) of models. This includes containerizing models using technologies like Docker and orchestrating their deployment at scale on platforms such as Kubernetes. A core responsibility is configuring and optimizing inference servers to ensure models serve predictions with high throughput and low latency. Consultants also implement comprehensive monitoring solutions to track model performance, data drift, and system health, ensuring high availability and quick issue resolution. The role demands a strong blend of technical skills. Proficiency in cloud platforms and infrastructure-as-code is fundamental. Deep hands-on experience with containerization and orchestration tools, particularly Kubernetes, is a standard requirement. Consultants must understand model serving frameworks and optimization techniques like quantization and pruning to enhance efficiency. Solid programming skills in Python, along with knowledge of API design and microservices architecture, are crucial. Equally important are the operational rigor and soft skills needed for mission-critical systems: expertise in incident, change, and problem management processes, coupled with strong client communication and problem-solving abilities to translate business needs into technical solutions. Typical requirements for these positions include a background in software engineering, DevOps, or cloud infrastructure, with direct experience in supporting production ML workloads. Employers seek individuals who are not only technically adept but also strategic thinkers capable of designing scalable AI platform strategies. As organizations increasingly rely on AI, the demand for skilled consultants who can bridge the gap between data science teams and operational IT continues to surge. For those passionate about building the robust backbone of modern AI applications, AI Operations Platform Consultant jobs offer a challenging and highly impactful career path.