AI Led Engineering Enterprise AI, Cloud & Platform Transformation (Onsite/Hybrid)

• Design, build, and maintain enterprise-scale AI platforms and cloud infrastructure.
• Develop and integrate AI solutions using Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and AI agents.
• Implement and operate AIOps and observability platforms to monitor system performance, reliability, and operational health.
• Lead cloud transformation initiatives across AWS, Azure, and/or Google Cloud Platform (GCP).
• Support production AI/ML environments, including model deployment, monitoring, lifecycle management, and optimization.
• Develop infrastructure automation, CI/CD pipelines, and platform engineering capabilities.
• Implement scalable infrastructure using technologies such as Kubernetes, Docker, Terraform, and service meshes.
• Integrate AI development tools and engineering copilots into software engineering workflows.
• Collaborate with architecture, operations, security, and delivery teams to ensure platform reliability, security, and scalability.
• Establish engineering standards and operational practices for mission-critical and regulated platforms.

The ideal candidate brings extensive experience across AI, DevOps, cloud engineering, platform engineering, and AIOps/observability. The role requires hands-on Python development, experience deploying AI/ML solutions in production, and the ability to design secure, scalable, and reliable platforms for mission-critical or regulated environments., * Minimum of 12 years of experience across AI, DevOps, cloud engineering, SRE, or platform engineering.

• Minimum of 3 years of hands-on experience with AI technologies and AI engineering stacks.
• Minimum of 5 years of experience designing enterprise platforms.
• Minimum of 3 years of experience implementing AIOps or observability platforms.
• Mandatory hands-on Python programming experience.
• Experience with enterprise cloud transformation initiatives.
• Experience supporting production AI/ML environments.
• Experience designing or operating mission-critical or regulated platforms.
• Strong collaboration and coordination skills across architecture, operations, security, and delivery teams.

Relevant Technologies and Technical Skills

AI and Machine Learning

• Generative AI and Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• AI agents, AgentCore, and Model Context Protocol (MCP)
• AWS Bedrock and Amazon SageMaker
• Vector databases
• Model deployment and lifecycle management
• Engineering copilots such as Kiro and GitHub Copilot

Cloud and Platform Engineering

• AWS, Microsoft Azure, and/or Google Cloud Platform (GCP)
• Infrastructure as Code and automation using Terraform
• CI/CD pipelines
• Kubernetes and Docker
• Service meshes
• Enterprise platform architecture and cloud modernization

AIOps, SRE, and Observability

• AIOps platform implementation
• Infrastructure and application monitoring
• Distributed tracing, logging, and metrics
• Datadog, Dynatrace, Splunk, Prometheus, and OpenTelemetry
• Production reliability, incident analysis, and performance optimization

Note: Candidates are not required to have experience with every named technology. Assessment should focus on transferable technical capabilities, relevant platform experience, and demonstrated implementation of AIOps or observability solutions., * Senior-level engineer or architect with a strong combination of AI engineering, enterprise platforms, and cloud operations experience.

• Hands-on Python developer with practical experience building or integrating AI solutions.
• Proven track record designing enterprise platforms and supporting production AI/ML workloads.
• Demonstrated experience implementing AIOps or observability platforms.
• Experience with at least one major cloud provider.
• Able to coordinate effectively across technical architecture, security, operations, and delivery teams.
• Comfortable evaluating and adopting relevant technologies without requiring experience with every product listed.