AI Led Engineering - Enterprise AI

AI Led Engineering – Enterprise AI, Cloud and Platform Transformation_New York, Bay Area, Dallas, or Chicago_Hybrid_Full-Time

Position: AI Led Engineering – Enterprise AI, Cloud and Platform Transformation

Locations: New York, Bay Area, Dallas, or Chicago

Work model: Hybrid, with no fully remote option.

Travel: approximately 25% when necessary.

Office attendance: the exact weekly onsite schedule remains pending.

Compensation guidance: USD 150,000–220,000 annual base, with potential flexibility.

AI Led Engineering – Enterprise AI, Cloud and Platform Transformation

Role overview: A senior technical leader combining enterprise AI engineering, cloud/platform architecture, DevOps/SRE and observability to modernize mission-critical systems and operational processes.

Key Responsibilities:

• Lead architecture and engineering for enterprise AI, cloud and platform transformation initiatives.
• Design production-ready AI/ML and GenAI capabilities and integrate them with enterprise platforms.
• Improve reliability, automation, deployment, monitoring and operational performance of mission-critical systems.
• Guide platform engineering, CI/CD, container orchestration, infrastructure automation and observability practices.
• Address security, governance, scalability and operational requirements in enterprise or regulated environments.

Required Experience and Technical Skills:

• Target profile: 12+ years across AI, DevOps, cloud, SRE or platform disciplines.
• Target profile: 3+ years hands-on AI stack experience and 5+ years enterprise platform design experience.
• Target profile: 3+ years AIOps/observability experience.
• Python is mandatory.
• Experience with enterprise cloud architecture and production AI/ML or GenAI systems.
• Knowledge of cloud platforms such as AWS, Azure or GCP; CI/CD; Kubernetes; Docker; Terraform; and platform automation.

Preferred / Additional Experience:

• GenAI/LLM/RAG, vector databases, model lifecycle, AWS Bedrock or SageMaker, and MCP/agent integrations.
• Tools such as Kiro, GitHub Copilot, Datadog, Dynatrace, Splunk, Prometheus or OpenTelemetry.
• Service mesh, SRE practices, AIOps and observability in complex enterprise environments.

What Candidates Should Be Able to Demonstrate

• A specific enterprise platform or AI transformation personally led or implemented.
• Evidence of hands-on Python and AI stack work, plus enterprise platform design.
• Examples of AIOps/observability, reliability improvements, automation, deployment outcomes or measurable operational impact.