Senior Lead Software Engineer, AI/ML Platform

What you'll do

• Build and maintain reusable AI/ML platform infrastructure and shared services for large-scale development and operations.
• Architect and operate secure cloud and container-based environments for training and inference, including GPU-intensive workloads.
• Design and implement infrastructure-as-code solutions to automate model deployment, environment provisioning, and release management.
• Develop production-grade APIs, SDK integrations, and workflows to support the AI application lifecycle.
• Optimize platform reliability, scalability, latency, and cost through orchestration and hardware acceleration.
• Establish operational best practices for monitoring, logging, observability, access controls, and incident response.
• Support enterprise LLM operationalization, including fine-tuning workflows, inference optimization, and evaluation.
• Drive the adoption of AI-assisted engineering practices and establish validation standards for secure coding and automated testing.

What we're looking for

• Formal training or certification on software engineering concepts and 5+ years of applied experience.
• Experience delivering secure, production-quality code in Python or Java.
• Strong foundations in distributed systems, microservices, and platform architecture/design principles.
• Proven ability to architect and operate cloud-native infrastructure on AWS and other major clouds.
• Demonstrated expertise with infrastructure-as-code tooling, specifically Terraform, in large-scale cloud environments.
• Hands-on experience with Docker and Kubernetes, including AWS EKS operations.
• Experience building or supporting production AI/ML platforms, including GPU infrastructure and model serving.
• Strong DevOps practices including CI/CD, release automation, automated testing, and observability.
• Experience with SQL/NoSQL databases, data integration, Linux, scripting, and networking fundamentals.
• Demonstrated experience leading the use of enterprise-authorized AI-assisted software development tools.
• Strong understanding of responsible AI use, data sensitivity, and coaching others on compliant usage patterns.
• Proficiency in Go or Python for automation, tooling development, or platform service implementation (preferred).
• Experience with MLOps frameworks and tools such as Kubeflow or MLflow (preferred).
• Working knowledge of ML frameworks like PyTorch, TensorFlow, or Hugging Face (preferred).
• Exposure to multi-cloud or hybrid cloud architectures and platform portability strategies (preferred).