We are seeking a visionary and hands-on AI Architect to join a leading global conglomerate. This is a high-impact role designed for a technical leader who thrives on the "Zero to One" journey. You will be responsible for the entire lifecycle of AI innovation—from identifying high-value business use cases and building rapid Proof of Concepts (PoCs) to architecting scalable, production-grade Agentic workflows.
The ideal candidate bridges the gap between deep technical execution and executive-level strategy, ensuring that AI investments translate into tangible business transformation.
Key Responsibilities
- End-to-End Productization: Lead the transition of AI initiatives from initial ideation and feasibility studies to full-scale production deployment.
- Architecting Agentic Systems: Design and implement sophisticated Multi-Agent Systems (MAS) and autonomous workflows that go beyond simple RAG (Retrieval-Augmented Generation).
- Hands-on Development: Maintain "skin in the game" by contributing to core codebase, optimizing LLM parameters, and refining system architecture.
- Strategic Roadmap: Collaborate with business unit heads across the conglomerate to identify high-impact AI use cases (Supply Chain, Customer Experience, Finance, etc.).
- Technical Governance: Establish best practices for AI safety, model evaluation (LLM-as-a-judge), cost management (token optimization), and scalability.
- Stakeholder Management: Present complex technical architectures to non-technical stakeholders, securing buy-in for large-scale AI investments.
Required Qualifications & Skills
Experience
- 12+ years of total experience in Software Engineering, Data Science, or Machine Learning.
- A proven track record of shipping end-to-end AI products in a corporate or high-growth startup environment.
- Minimum 2+ years of focused experience in Generative AI, specifically with Large Language Models (LLMs).
Technical Proficiencies
- GenAI Ecosystem: Deep expertise in frameworks like LangChain, LlamaIndex, CrewAI, or AutoGen.
- Agentic Frameworks: Experience building autonomous agents with tool-calling capabilities and complex reasoning loops.
- Infrastructure: Proficiency in Python, vector databases (Pinecone, Weaviate, Milvus), and cloud AI suites (Azure AI Studio, AWS Bedrock, or GCP Vertex AI).
- Productionization: Strong understanding of MLOps/LLMOps, CI/CD pipelines, and containerization (Docker/Kubernetes).