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Chief AI System Architect
3 weeks ago
Job Title: AI Architect - Generative Systems
Experience: 8-12 Years
We are seeking a seasoned Python architect with a strong foundation in backend engineering and hands-on experience in generative AI. The ideal candidate will have 8–10 years of core Python development experience, along with 2–2.5 years of focused engineering work in GenAI and microservices.
This role is ideal for individuals who are passionate about designing scalable AI solutions, writing production-grade code, and enabling DevOps practices for AI-powered microservices in enterprise environments.
Primary Responsibilities:
- Design and implement Python-based microservices integrating GenAI capabilities
- Develop GenAI-powered solutions using Retrieval-Augmented Generation (RAG) pipelines and LLM orchestration engines
- Implement RESTful APIs and backend services using FastAPI/Flask integrated with OpenAI/Azure OpenAI or open-source models
- Collaborate with DevOps engineers to enable CI/CD, monitoring, and observability for GenAI pipelines
- Work with ML engineers and data scientists to optimize GenAI system performance and scalability
- Lead design sessions, code reviews, and mentor junior engineers on architectural best practices
- Ensure system reliability, scalability, and security across GenAI-enabled services
Core Skills Required:
- Expert-level Python development skills including experience with FastAPI, Flask, asyncio
- Strong understanding of microservices architecture and distributed systems
- Hands-on experience with GenAI tools: LangChain, LlamaIndex, Hugging Face Transformers
- Practical knowledge of deploying LLMs on cloud platforms (Azure OpenAI preferred)
- Experience with REST API design and integration with external AI services
- DevOps practices: CI/CD with GitHub Actions, Docker, Kubernetes, Terraform
- Proven ability to design scalable, maintainable, and testable AI-driven applications
Secondary / Nice-to-Have Skills:
- Experience with agentic orchestration systems such as Autogen, Semantic Kernel, LangGraph
- Exposure to fine-tuning open-source LLMs and deployment in containerized environments
- Understanding of AI observability, performance tuning, and system logging
- Background in MLOps and collaboration with Data Science/ML teams
- Familiarity with agile development and cross-functional engineering collaboration
