Application Support Architect – Python & AI

We are seeking a highly skilled Application Architect – AI Solutions to shape, design, and deliver modern AI-enabled applications and SaaS-style services for deployment within enterprise client environments. This role is ideal for an experienced architect who can collaborate across architecture, engineering, and client stakeholder teams to translate business requirements into secure, scalable, and practical solution designs. The ideal candidate will have proven expertise in architecting AI solutions, platform services, or productized applications, along with strong knowledge of cloud-native architectures, APIs, enterprise integration, security, and deployment models.

Key Responsibilities

Architect AI-enabled applications, SaaS-style platforms, and reusable services for client deployments. Define end-to-end solution architectures covering application design, APIs, integrations, data flows, security, hosting, and operational considerations. Collaborate with engineering teams to guide implementation using Python, FastAPI, Java , and cloud-native technologies. Design AI orchestration, agentic workflows, and modern AI integration patterns. Assess client environments and recommend deployment models across cloud, hybrid, and client-managed infrastructure. Ensure solutions are secure, scalable, maintainable, and aligned with enterprise architecture standards. Provide technical leadership through architecture reviews, design guidance, and engineering best practices. Work closely with client technical stakeholders to explain architectural decisions, implementation approaches, and technical trade-offs.

Required Skills & Experience

Proven experience as an Application Architect, Solution Architect, or Lead Engineer delivering enterprise-grade applications. Experience architecting AI-enabled solutions, AI platforms, digital products, or SaaS applications . Strong application development experience in Python . Working knowledge of Java is an added advantage. Hands-on experience with FastAPI or equivalent API development frameworks. Experience with modern AI orchestration and agent frameworks such as LangGraph or similar technologies. Understanding of Model Context Protocol (MCP), Agent-to-Agent (A2A) , or comparable AI communication and integration patterns. Strong expertise in:

API Design Microservices Architecture Event-Driven Architecture Enterprise Integration Patterns

Solid understanding of enterprise security practices, including:

Authentication & Authorization Data Protection Secrets Management Secure Application Deployment

Experience deploying enterprise applications into client-managed infrastructure or cloud environments. Exposure to AWS and Microsoft Azure (experience with Google Cloud Platform (GCP) is desirable but not mandatory). Excellent collaboration and communication skills with engineers, architects, product owners, and client stakeholders.

Preferred Qualifications

• Experience with containerization and orchestration technologies such as Docker and Kubernetes . Knowledge of CI/CD pipelines , DevOps practices , and Infrastructure as Code (IaC) . Understanding of observability, monitoring, logging, and operational support frameworks. Hands-on experience with:

• Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) AI Agents Vector Databases AI Governance

• Experience designing reusable platforms, accelerators, or services that can be leveraged across multiple client engagements.

Ideal Candidate Profile The ideal candidate is a hands-on Application Architect who understands how modern AI and cloud-native applications are built while possessing the strategic vision to define scalable architectures, mentor engineering teams, and confidently engage with enterprise clients. You should be comfortable balancing technical leadership with practical delivery and driving the successful implementation of AI-powered solutions.

Technical Skills

Agentic AI Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Python FastAPI Java LangGraph Model Context Protocol (MCP) Agent-to-Agent (A2A) API Design Microservices Event-Driven Architecture AWS Microsoft Azure Google Cloud Platform (Preferred) Docker Kubernetes CI/CD DevOps Infrastructure as Code (IaC)

Agentic AI, LLMs, RAG, Python