EMPLOYMENT
Full-time
COMPENSATION
$160k - 200k/yr
ARRANGEMENT
On-site
At a glance
Lead the architecture, deployment, and scaling of AI-powered applications, including LLM and agentic workflows, while driving secure, cost-efficient, cloud-native AI engineering across teams.
Summarized by AI from the original posting
What you'll do
- Develop, deploy, and scale AI-powered applications
- Design AI-first architectures with microservices, event-driven systems, and real-time AI workflows
- Deploy, optimize costs for, and fine-tune AI models
- Develop LLM, agentic AI, and autonomous AI-driven workflows
- Apply security, compliance, and ethical practices to AI development
- Lead AI-first engineering transformations across teams
- Use MLOps best practices for cloud-native AI deployments
Requirements
- 013+ years leading AI-powered application development
- 02Experience developing, deploying, and scaling AI-powered applications
- 03Expertise in AI-first architectures
- 04Hands-on experience with LLMs, agentic AI models, and autonomous AI-driven workflows
- 05Strong skills in AI model deployment, cost optimization, and fine-tuning
- 06Understanding of security, compliance, and ethical considerations in AI development
- 07Experience leading AI-first engineering transformations
- 08Experience with cloud-native AI deployment
Skills
Ai Powered ApplicationsOpen ModelsAi ApisMicroservicesEvent Driven SystemsReal Time Ai WorkflowsLlmsAgentic Ai ModelsAutonomous Ai WorkflowsAi Model DeploymentCost OptimizationFine-tuning
Full description
Experience:
· 8+ years of software engineering experience, including 3+ years leading AI-powered application development.
· Bachelor’s degree in Computer Science, Computer Engineering, or related field.
· Proven experience developing, deploying, and scaling AI-powered applications (provide examples of integrations using open models or APIs).
· Expertise in designing AI-first architectures, including microservices, event-driven systems, and real-time AI workflows.
· Hands-on experience with LLMs, agentic AI models, and autonomous AI-driven workflows.
· Strong skills in AI model deployment, cost optimization, and fine-tuning.
· Deep understanding of security, compliance, and ethical considerations in AI development.
Additional Items of Interest:
· Experience leading AI-first engineering transformations across teams.
· Contributions to open-source AI projects or AI thought leadership.
· Experience with agentic AI frameworks, composable AI solutions, and MLOps best practices.
· Industry engagement through conferences, research initiatives, or AI-first development communities.
· Cloud-native AI deployment experience (Docker, Kubernetes, cloud platforms).