Software AI Engineer

AI Platform & Architecture Leadership

•                     Lead the architecture and development of AI platforms supporting agents, workflows, RAG systems, and LLM-based applications.

•                     Define best practices for AI system design, model orchestration, inference pipelines, and runtime infrastructure.

•                     Drive the evolution of AgentOps frameworks for managing AI agents at scale.

Engineering Leadership

•                     Provide technical leadership and mentorship to a team of engineers working on AI infrastructure and platform systems.

•                     Collaborate cross-functionally with AI Research, Product, and Platform teams to deliver production-grade AI solutions.

•                     Establish engineering standards, design patterns, and development practices.

AI Systems & AgentOps

•                     Design and manage AI agent architectures, workflow orchestration, and multi-agent systems.

•                     Build and operate Model Gateways and LLM routing layers across providers (OpenAI, Azure OpenAI, Claude, Gemini, etc.).

•                     Lead development of RAG systems with vector databases and retrieval pipelines.

•                     Optimize latency, throughput, and cost of AI workloads.

Platform & Infrastructure Engineering

•                     Own Kubernetes-based platforms for scalable AI workloads (GKE preferred).

•                     Design and implement cloud-native architectures across GCP (primary), AWS/Azure (secondary).

•                     Lead Infrastructure-as-Code (Terraform) and platform automation initiatives.

•                     Establish CI/CD and GitOps-based deployment models for AI systems.

Reliability, Observability & Operations

•                     Define and implement SRE practices including monitoring, ing, and incident management.

•                     Architect observability using OpenTelemetry, Prometheus, Grafana, ELK, or Cloud Monitoring.

•                     Drive production readiness, scalability planning, and disaster recovery strategies.

Enterprise Deployments & Security

•                     Ensure AI platform compliance with enterprise-grade security and governance standards.

•                     Implement IAM, RBAC, SSO, secrets management, and network security controls.

•                     Support customer deployments across cloud, hybrid, and on-prem environments.

Mandatory Skills & Experience

AI Engineering & Systems

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