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, scikit‑learn, 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.