AI Platform Engineer
Hybrid · Lisbon, Lisbon, Portugal
Job Summary
AI Platform Engineer to productionise ML and AI systems across core banking products, focusing on agentic workflows. Build and operate production MLOps and LLMOps workflows, design CI/CD pipelines for training, packaging, testing, release, rollback, and controlled promotion. Containerise and deploy AI services using Kubernetes, serverless, or managed container patterns. Develop production-grade agentic workflows with tool calling, state management, orchestration, and failure recovery. Implement observability for models and agents (latency, reliability, cost, token usage, tracing, drift, quality signals). Integrate AI services with product APIs, security controls, and operational workflows. Support model registry, experiment tracking, versioning, reproducibility, and lifecycle governance. Define reusable engineering patterns for retrieval, memory, model routing, human-in-the-loop intervention, and agent execution. Optimize for reliability, scalability, cost, and operational simplicity.
Required Qualifications
- Strong hands-on experience in MLOps, platform engineering, or production AI infrastructure
- Solid understanding of cloud-native software delivery
- Experience with Docker, Kubernetes, CI/CD, GitHub Actions, Terraform, or similar infrastructure-as-code approaches
- Experience with MLflow, model versioning, deployment pipelines, and runtime monitoring
- Experience deploying LLM or GenAI workloads into production, not just building prototypes
- Practical experience with agent frameworks such as LangGraph, LangChain, AutoGen, CrewAI, or similar
- Ideal: Experience with Azure AI Foundry, Azure OpenAI, Azure Container Apps, and Azure AI Search
- Experience with Databricks, feature engineering platforms, model serving, and data platform integration
- Familiarity with MCP, tool registries, and enterprise connector patterns
- Experience with vector databases, semantic retrieval, and retrieval-augmented generation operating patterns
- Experience in banking, payments, lending, or other regulated environments
- Experience with policy enforcement, audit logging, model risk controls, and environment separation
- Tech stack examples: Azure AI Foundry, Databricks, MLflow, LangGraph, Python, Docker, Kubernetes, Azure Container Apps, AKS, Azure Functions, Azure Monitor, Application Insights, Azure AI Search, GitHub Actions, Terraform, vector stores
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