What are the responsibilities and job description for the AI/ML Engineering Lead position at International Software Systems, Inc?
Position Overview:
The Senior AI/ML Platform Engineer will design, implement, operate, and continuously improve the shared enterprise AI/ML platform. The role focuses on platform automation, MLOps, CI/CD, cloud infrastructure, observability, reliability engineering, and cost optimization.
Key Requirements:
- 10 years of experience in platform engineering, MLOps, or data/software engineering, including 2 years working with production ML systems.
- Bachelor's degree in Computer Science, Information Systems, Engineering, Business, or a related technical discipline; master's degree preferred.
- Strong proficiency in Python and production-grade software development.
- Experience designing and improving ML CI/CD pipelines, automation frameworks, and deployment processes.
- Experience with AWS, Azure, or Google Cloud Platform, including cloud resource management and cost optimization.
- Advanced Kubernetes experience, including cluster management, resource quotas, autoscaling, and workload scheduling.
- Experience with Prometheus, Grafana, Datadog, OpenTelemetry, or equivalent observability tools.
- Production-scale experience with Terraform and Ansible.
- Strong understanding of the ML lifecycle, experiment tracking, model registries, feature stores, model serving, and drift monitoring.
- Experience with automated model validation, testing, A/B testing, canary deployments, rollback, and production releases.
- Experience developing internal SDKs, CLI tools, reusable templates, APIs, and standardized deployment workflows.
- Strong troubleshooting, incident response, root-cause analysis, and technical documentation skills.
Preferred Experience:
- Shared platforms or internal developer platforms (IDPs).
- LLM serving technologies such as vLLM, Text Generation Inference (TGI), and managed LLM APIs.
- Government, regulated, or FedRAMP-compliant environments.
- NIST AI Risk Management Framework and responsible AI governance.
- FinOps practices and tools such as Infracost and AWS Cost Explorer.
- GitOps, platform APIs, developer tooling, and infrastructure observability.