Senior MLOps Platform Engineer {S}

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior MLOps Platform Engineer based in United States. This role is an opportunity to build and operate the infrastructure powering next-generation Agentic AI services at scale. You will own the end-to-end MLOps foundation across on-premise Kubernetes environments and AWS. Working alongside data scientists, product engineers, and SREs, you will turn experimental AI models into reliable production services. Your work will shape architecture, automation, observability, security, and performance for mission-critical applications. You will help enable thousands of concurrent AI agents through highly available and governed platform capabilities. The environment emphasizes strong engineering practices, reusable tooling, and continuous improvement. A flexible schedule is also available within the pay period to support work-life balance. Accountabilities: - Design, implement, and operate a unified MLOps platform spanning on-premise Kubernetes clusters and AWS, enabling rapid onboarding and consistent governance for Agentic AI services. - Develop reusable GitLab CI and CI/CD pipelines covering model packaging, containerization, automated testing, canary deployments, and rollbacks. - Build and maintain observability, monitoring, and alerting capabilities using tools such as Prometheus, Grafana, OpenTelemetry, and CloudWatch to track latency, throughput, resource utilization, and data drift. - Create self-service CLIs, SDKs, and dashboards that allow data science and product teams to register models, configure inference endpoints, and manage versions with minimal DevOps support. - Architect and maintain robust data pipelines for training data, model artifacts, and inference logs across S3 and on-premise object storage.

- Partner with research and product engineering teams to transform AI prototypes into production-grade services with strong reproducibility, security, compliance, and maintainability. - Optimize inference performance through GPU/CPU scaling, model quantization, batching strategies, and other techniques for high-throughput workloads. - Champion security, cost efficiency, disaster recovery, and operational best practices across hybrid infrastructure, including IAM, network policies, and secret management. - Mentor junior engineers and contribute to technical documentation, knowledge sharing, skills development, and engineering reviews. Requirements: - Bachelor’s degree in Computer Science, Engineering, or a related technical field. - 5+ years of experience building and operating production-grade software infrastructure, preferably across hybrid on-premise and cloud environments. - Deep expertise with Kubernetes, including cluster provisioning, Helm, operators, custom resources, and container runtimes such as Docker and OCI. - Hands-on experience with AWS services including EKS, SageMaker, S3, IAM, CloudWatch, and Step Functions, with the ability to connect on-premise resources to AWS through VPN or Direct Connect. - Strong Python software engineering skills plus proficiency in at least one compiled language such as Go, Rust, or Java for developing platform components and SDKs. - Experience with CI/CD and GitOps tooling such as Argo CD, Flux, GitLab, or GitHub Actions. - Strong understanding of distributed systems, including consensus, fault tolerance, load balancing, and performance tuning for high-throughput, low-latency inference pipelines. - Experience with data engineering technologies such as Airflow, Prefect, Kafka, Spark, or Flink and the ability to build robust, versioned data pipelines. - Familiarity with observability platforms including Prometheus, Grafana, OpenTelemetry, or ELK, along with experience defining meaningful SLIs and SLOs for AI services.