Senior MLOps Engineer - Contract to Hire - No 3rd Parties

Dice is the leading career destination for tech experts at every stage of their careers. Our client, SilverSearch, Inc., is seeking the following. Apply via Dice today! 100% remote role - work on EST We are seeking a Senior MLOps Engineer to support large-scale production machine learning environments focused on text, image, and video processing workloads in AWS. This is a highly operational and infrastructure-focused role. The ideal candidate has hands-on experience deploying, monitoring, scaling, and optimizing ML systems in production environments particularly within AWS SageMaker ecosystems. This is NOT a data science or model research role. The focus is production reliability, deployment governance, infrastructure scalability, observability, and operational efficiency. Responsibilities ML Deployment & Operations • Design, deploy, and support end-to-end production ML pipelines • Manage ML promotion across Dev, QA, and Production environments • Implement deployment standards, rollback strategies, and recovery mechanisms • Support containerized inference and orchestration patterns AWS & Infrastructure Management • Configure and manage AWS SageMaker pipelines, endpoints, and monitoring • Optimize GPU and CPU infrastructure selection and scaling • Benchmark infrastructure performance and tune autoscaling behavior • Perform load testing and production infrastructure optimization Monitoring & Reliability • Implement monitoring, alerting, observability, and drift detection • Track latency, throughput, error rates, and model/data drift • Build A/B testing and controlled rollout frameworks • Ensure governance, reproducibility, security, and cost efficiency Large-Scale ML Workloads • Support production ML systems across text, image, and video workloads • Manage high-throughput infrastructure and large-scale data movement • Prevent compute, networking, and storage bottlenecks • Support systems processing hundreds of thousands of requests daily Collaboration • Partner closely with ML Engineers, Platform Engineering, DevOps, and Data teams • Operationalize ML models into stable production systems • Help drive scalability, reliability, and infrastructure best practices Required Qualifications • Strong hands-on experience operating production ML systems at scale • Deep AWS SageMaker experience including: • Pipelines • Endpoints • Monitoring • Multi-environment deployments • Experience operationalizing PyTorch and TensorFlow models • Experience with containerized ML deployment and orchestration • Experience optimizing GPU/CPU infrastructure for ML workloads • Strong monitoring and observability experience • Experience implementing deployment governance and rollback strategies Strongly Preferred • Experience supporting: • Transformer-based NLP systems • Computer vision workloads • Ranking/reranking systems • Familiarity with: • ANN systems • HNSW indexing • Large-scale neural network operational workloads • Experience supporting high-volume text, image, and video dataset • Candidates must be able to work directly on W2 or approved independent consulting arrangements. • NO 3RD PARTY FIRMS, LAYERED VENDORS, OR STAFFING PASSTHROUGHS. • Third-party submissions will NOT be reviewed or responded to. If interested, please send: • Updated resume • Current location • Work authorization status • Availability • Hourly rate expectations