AI Led Engineering – Enterprise AI

Bestinfo Systems LLC; Chicago (IL); Hybrid; USD 150,000 - 220,000; Full time

Job summary

Bestinfo Systems LLC in the United States seeks a senior technical leader to drive AI-led engineering for enterprise AI, cloud and platform transformation across mission-critical systems. You will design production-ready AI/ML and GenAI capabilities, guide CI/CD, Kubernetes and observability, and ensure security, governance and reliability in complex environments.

Required: 12+ years in AI/DevOps/cloud/SRE or platform roles; 3+ years hands-on AI; 3+ years observability; Python mandatory; cloud

Qualifications

• 12+ years across AI, DevOps, cloud, SRE or platform disciplines.
• 3+ years hands-on AI stack experience and 5+ years enterprise platform design.
• 3+ years AIOps/observability experience.

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.

Skills

Python programming

AI/ML stack

DevOps

Cloud architecture

SRE

Platform engineering

Observability

Tools

Kiro

GitHub Copilot

Datadog

Dynatrace

Splunk

Prometheus

OpenTelemetry

Docker

Kubernetes

Terraform

Job description

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.