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AI/ML Operations Support Engineer
Location
United States
Posted
41 days ago
Salary
0
Seniority
Mid Level
No structured requirement data.
Job Description
AI/ML Operations Support Engineer
Prosum Inc.
Role Description We are seeking an AI/ML Operations Support Engineer to support enterprise machine learning and generative AI platforms in production environments. This role is focused on platform operations, production support, monitoring, deployment support, troubleshooting, automation, and governance across cloud-based AI ecosystems. This is not a pure machine learning development role. The ideal candidate will have experience supporting AI/ML solutions in production and ensuring reliability, performance, scalability, and operational readiness. Qualifications - 3-7 years of experience supporting cloud-based applications and platforms. - Experience supporting machine learning, generative AI, or advanced analytics platforms in production environments. - Strong cloud experience, preferably within AWS or similar enterprise cloud platforms. - Experience supporting APIs, integrations, and distributed systems. - Ability to work independently while collaborating effectively across teams. - Strong troubleshooting and problem-solving skills. Requirements - Experience supporting cloud-hosted applications and services in production environments. - Experience administering or supporting AI/ML platforms, model-serving environments, or generative AI solutions. - Hands-on experience supporting MLOps and/or LLMOps processes. - Experience deploying, maintaining, monitoring, and troubleshooting machine learning models in production. - Familiarity with model-serving infrastructure, inference endpoints, vector search technologies, and AI application support. - Experience with observability, model monitoring, alerting, performance management, drift detection, or governance processes. - Experience using Python or similar scripting languages for automation and operational support. - Experience supporting containerized environments and orchestration platforms. - Familiarity with CI/CD pipelines and infrastructure automation. - Understanding of machine learning lifecycle management from deployment through retirement. Benefits - Strong production support and incident management experience. - Ability to identify root causes and resolve complex technical issues. - Experience creating operational dashboards, metrics, monitoring, and alerting frameworks. - Excellent communication and collaboration skills. - Experience working with cross-functional engineering, data, and business teams. - Experience in Agile delivery environments. Key Responsibilities - Support enterprise AI/ML and generative AI platforms to ensure high availability, performance, and reliability. - Monitor production AI workloads and proactively address operational issues. - Troubleshoot model deployments, inference services, platform infrastructure, and data pipeline issues. - Participate in operational support and incident response activities. - Support model deployment, promotion, rollback, and lifecycle management processes. - Maintain version control, deployment governance, and operational standards. - Validate deployment readiness through testing and operational reviews. - Implement and maintain monitoring solutions for model health, prediction quality, latency, throughput, utilization, and operational compliance. - Create dashboards, reporting, and alerting capabilities to improve visibility and reliability. - Support governance, security, and responsible AI requirements. - Develop automation solutions to improve operational efficiency and reduce manual effort. - Support CI/CD processes and infrastructure automation initiatives. - Contribute to MLOps, LLMOps, observability, and platform reliability best practices. - Partner with Data Scientists, ML Engineers, Platform Engineers, Data Engineers, and Application Teams. - Collaborate with Cloud, Infrastructure, Security, and Architecture teams to support scalable AI solutions. - Contribute to documentation, knowledge sharing, and operational readiness efforts. Ideal Candidate The successful candidate will have a strong operations mindset, understand how AI/ML solutions function in production environments, and be comfortable troubleshooting across infrastructure, platform, application, and model layers. They should be capable of working independently, adapting to evolving technologies, and supporting multiple AI/ML platforms rather than a single vendor-specific solution.
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