Machine Learning Engineer (Agentic AI)
Responsibilities:
- Multi-Agent Orchestration:
Design and productionalize complex multi-agent systems, focusing on agent communication, task decomposition, and state management.
- Enterprise Deployment:
Take full ownership of deploying agentic workflows into high-availability corporate environments, ensuring robust performance and global scalability.
- API & UI Integration:
Architect high-performance FastAPI endpoints and collaborate with vendors to "connect the dots" between the AI agent and the front-end UI.
- Production MLOps:
Build and maintain CI/CD pipelines, Docker containers, and monitoring systems on Azure to track agent reasoning and system health.
- Data Strategy:
Optimize data ingestion and memory grounding for agents using FactorDB, SQL, and NoSQL databases to support structured and unstructured data.
Qualifications:
- Experience:
3–5 years in ML/Software Engineering with proven experience deploying AI solutions within a large-scale enterprise or corporate environment.
- Agentic Frameworks:
Hands-on experience building autonomous or semi-autonomous workflows (e.g., LangGraph, CrewAI) and managing real-time AI interactions.
- Advanced API Skills:
Expert proficiency in FastAPI for building asynchronous back-end services and integrating with third-party vendor tools.
- Technical Stack:
Proficient in Python, Spark, and core data science packages (PyTorch, sklearn, Pandas) for model optimization and data processing.
- Engineering Rigor:
Strong background in Docker, version control, and debugging distributed systems where multiple AI components interact.
Argyll Scott Asia is acting as an Employment Business in relation to this vacancy.