Senior MLOps Platform Engineer

CACI is an innovative company focused on building the next generation of Agentic AI products. They are seeking a Senior MLOps Platform Engineer to design and operate a unified MLOps platform that supports both on-premise and cloud environments, ensuring the scalability and reliability of AI services.

Responsibilities:

• Design, implement, and operate a unified MLOps platform that supports both on-premise Kubernetes clusters and AWS. The platform should enable rapid onboarding of new Agentic AI services and provide consistent governance across environments
• Develop reusable CI/CD pipelines (GitLab CI) for model packaging, containerization, automated testing, canary releases, and rollbacks
• Build observability, monitoring, and alerting stacks (Prometheus, Grafana, OpenTelemetry, CloudWatch) to track inference latency, throughput, resource utilization, and data drift for real time and batch workloads
• Create self-service tooling (CLI, SDKs, UI dashboards) that allows data science and product teams to register models, define inference endpoints, and manage versioning without deep DevOps involvement
• Architect and maintain data pipelines that feed training data, model artifacts, and inference logs into a governed data lake (S3, on prem object store)
• Collaborate with research and product engineers to translate experimental Agentic AI prototypes into production grade services, ensuring reproducibility, security, and compliance
• Drive performance optimization for inference workloads (GPU/CPU scaling, model quantization, batching strategies)
• Champion best practices in security (IAM, network policies, secret management), cost efficiency, and disaster recovery for the hybrid infrastructure
• Mentor junior engineers and contribute to internal knowledge bases, upskilling, and review processes

Requirements:

• Must be a U.S. Citizen
• BS in computer science or related engineering field
• 5+ years of experience building and operating production grade software infrastructure, preferably in a hybrid onprem / cloud environment
• Deep expertise with Kubernetes (cluster provisioning, Helm, operators, custom resources) and container runtimes (Docker, OCI)
• Hands on experience with AWS services (EKS, SageMaker, S3, IAM, CloudWatch, Step Functions) and the ability to bridge onprem resources with AWS via VPN/Direct Connect
• Strong software engineering skills in Python and at least one compiled language (Go, Rust, or Java) for building platform components and SDKs
• Proficiency with CI/CD and GitOps tooling (Argo CD, Flux, Gitlab, GitHub Actions, or similar)
• Solid understanding of distributed systems (consensus, fault tolerance, load balancing) and experience tuning high throughput, low latency inference pipelines
• Experience with data engineering frameworks (Airflow, Prefect, Kafka, Spark, Flink) and building robust, versioned data pipelines
• Familiarity with observability stacks (Prometheus, Grafana, OpenTelemetry, ELK) and the ability to define meaningful SLIs/SLOs for AI services
• Track record of collaborating with research or product teams to move prototypes to production, translating experimental code into maintainable services
• Strong problem solving mindset, excellent written and verbal communication, and a passion for building scalable AI platforms
• Working knowledge of Scrum and Agile software development methodology