MLOps Engineer

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September 12

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Pierce Professional Resources

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Enterprise • Recruitment • Consulting

Pierce Professional Resources is a company specializing in technical and workforce solutions aimed at helping businesses evolve. They offer a range of services including technical staffing, automation and AI, cloud solutions, data modernization, digital transformation, and UI/UX consulting. Additionally, they provide agile leadership coaching, PMO services, and assist with transactional mergers and acquisitions. With over 30 years of experience, Pierce Professional Resources emphasizes delivering exceptional technical solutions and personalized services for their clients, aiming to optimize operations and drive innovation. They focus on building partnerships by deeply understanding client needs and providing customized solutions to achieve success.

11 - 50 employees

Founded 2003

🏢 Enterprise

🎯 Recruiter

📋 Description

• We are seeking a highly skilled MLOps Engineer to design, build, and operate scalable machine learning infrastructure that supports modern AI applications. • Build and operate robust data, embedding, and prompt pipelines to support production AI/ML workloads. • Maintain a secure and scalable system for managing AI agent identity, versioning, and registration. • Deliver automated workflows for model deployment and infrastructure provisioning using modern DevOps tooling. • Design and implement primitives for distributed coordination and orchestration of ML agents and services. • Implement observability, monitoring, and guarded execution frameworks to ensure safe and reliable AI system behavior.

🎯 Requirements

• Strong experience with MLOps, DevOps, or SRE practices in production environments. • Hands-on expertise with CI/CD pipelines and Infrastructure as Code (Terraform, Pulumi, etc.). • Solid understanding of data engineering, feature/embedding pipelines, and ML model deployment. • Familiarity with observability tooling (Prometheus, Grafana, ELK, OpenTelemetry, etc.). • Experience with distributed systems and coordination mechanisms (e.g., Kubernetes, service meshes, message queues). • Proficiency in one or more languages: Python, Go, or similar. • Bonus: Knowledge of LLM ops, prompt engineering infrastructure, or agent frameworks.