Platform Lead / Architect - AI Platform Engineering
Role details
Job location
Tech stack
Job description
Photon is seeking a highly experienced Platform Lead / Architect - AI Platform Engineering to lead the architecture, design, and delivery of Tulip AI, a unified AI-powered employee support platform.
Tulip AI is an enterprise-grade AI platform that streamlines employee support across frontline and headquarters teams through a single AI-driven experience. The platform leverages Generative AI, workflow orchestration, enterprise integrations, automation, and cloud-native technologies to improve operational efficiency and accelerate issue resolution., * Lead the overall architecture, technical strategy, and solution design for Tulip AI and related digital platforms.
- Design and deliver scalable, secure, resilient, and cloud-native enterprise solutions.
- Architect backend services, APIs, and microservices using Python and FastAPI.
- Drive implementation of Generative AI, agentic AI capabilities, workflow orchestration, and intelligent automation solutions.
- Design enterprise integration architectures connecting HR, IT, identity management, operational systems, and third-party platforms.
- Define and enforce standards for APIs, microservices, event-driven systems, and integration patterns.
- Collaborate with Product, Engineering, UX, and Business stakeholders to align technical solutions with business objectives.
- Support product roadmap planning, backlog prioritization, and Agile delivery processes.
- Establish engineering best practices around scalability, performance, security, observability, monitoring, and reliability.
- Drive adoption of AI-assisted development, automation, and engineering productivity tools.
- Provide technical leadership, architectural governance, and mentorship across engineering teams.
- Lead cloud deployment strategies leveraging Kubernetes, CI/CD pipelines, Infrastructure as Code (IaC), and DevOps practices.
- Ensure production readiness through testing strategies, monitoring, logging, and operational excellence., Programming & Backend
- Python
- FastAPI
- REST APIs
- Microservices
AI & Automation
- Generative AI
- Agentic AI
- AI Orchestration Frameworks
- Workflow Automation
- LLM Integrations
- RAG Architectures
Cloud & Infrastructure
- AWS / Azure / Google Cloud Platform
- Kubernetes
- Docker
- Terraform
- Infrastructure as Code
DevOps
- CI/CD
- GitHub Actions
- Jenkins
- Azure DevOps
- Monitoring & Observability
Architecture
- Enterprise Architecture
- Distributed Systems
- Event-Driven Architecture
- Integration Architecture
Requirements
The ideal candidate will have deep expertise in enterprise architecture, AI platforms, cloud-native systems, distributed architectures, Python-based backend development, and platform engineering. This role requires strong technical leadership and the ability to drive architectural decisions across multiple engineering teams., * 12+ years of experience in Software Engineering, Platform Architecture, Enterprise Architecture, or Solution Architecture.
- Strong expertise in Python development and FastAPI-based microservices architecture.
- Extensive experience designing and implementing cloud-native enterprise platforms.
- Hands-on experience with Generative AI, AI orchestration frameworks, AI agents, workflow automation, and enterprise AI solutions.
- Strong understanding of:
- Microservices Architecture
- REST APIs
- Event-Driven Systems
- Integration Architecture
- Experience with one or more cloud platforms:
- AWS
- Microsoft Azure
- Google Cloud Platform (Google Cloud Platform)
- Expertise in:
- Kubernetes
- Docker/Containerization
- CI/CD Pipelines
- DevOps Practices
- Infrastructure as Code (Terraform, CloudFormation, etc.)
- Strong knowledge of:
- Enterprise Security
- Authentication & Authorization
- OAuth2/OpenID Connect
- Secure API Design
- Experience working in Agile/Scrum environments.
- Excellent communication, stakeholder management, and technical leadership skills., * Experience building enterprise-scale AI platforms and digital employee experience solutions.
- Familiarity with AI frameworks such as LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen, or similar orchestration platforms.
- Experience with vector databases, RAG architectures, LLM integrations, and AI governance frameworks.
- Experience implementing observability solutions using Prometheus, Grafana, Datadog, OpenTelemetry, or similar tools.
- Prior experience leading distributed engineering teams and large-scale transformation initiatives.