Senior MLOps Platform Engineer - ARKA Group, LP

ARKA Group L.P. (“ARKA”) is an advanced technologies company serving the U.S. military, intelligence community, and commercial space industry delivering next-generation solutions to support the national security space enterprise. Built on more than six decades of excellence, ARKA brings modern approaches and a culture of innovation to the challenges of today.

Join the ARKA team to learn how Beyond Begins Here. Discover your next career opportunity now!

Position Overview:

Our AI Center of Excellence builds the next generation of Agentic AI products that autonomously reason, plan, and act on behalf of our customers.  To deliver these capabilities at scale, we need a platform engineering group that provides a robust, secure, and highly available MLOps foundation across both on premise clusters and AWS.  The team works closely with data scientists, product engineers, and SREs to turn experimental models into reliable services that power mission critical applications.

In support of work/life balance, many positions are available for a flexible schedule within the pay period. Ask us about the opportunity for flex scheduling if that’s of interest to you.

Why join us

• Shape the end-to-end lifecycle of cutting-edge AI services—from model training to production inference.
• Influence architecture decisions for a hybrid cloud environment that will serve thousands of concurrent agents.
• Collaborate with world-class researchers and product teams while enjoying a strong engineering culture focused on automation, observability, and reliability.

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)
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