Forward Deployed AI Engineer

About this course

The Forward Deployed AI Engineer (FDAIE) is rapidly emerging as one of the most vital, high-value technical roles in the artificial intelligence landscape.

The daily job of an FDAIE is to integrate AI into the customer's IT environment. To become an FDAIE, the following skills are required:1. Technical2. Consulting3. AI4. Product Knowledge 1. Technical: Coding, Integration, and DeploymentCodingPython: The industry standard for AI orchestration, fine-tuning, RAG pipelines, model evaluation, and backend glue code.

JavaScript / TypeScript: Essential for front-end interface integrations, node-based API middleware, and custom web applications.

SQL: Crucial for querying structured databases, managing data pipelines, and preparing client data for RAG/fine-tuning.

IntegrationAPI Integration: Connecting AI models and vector datastores to legacy enterprise systems, CRMs, ERPs, and internal databases via REST, GraphQL, or gRPC APIs.

Model & Agent Orchestration: Deploying Retrieval-Augmented Generation (RAG) pipelines, managing context windows, and configuring tool-calling agentic protocols.

Deployment

Methodologies such as DevOps, MLOps, LLMOps, AgentOpsLanding Zone Preparation: Structuring secure, scalable "landing zones" within client IT environments (AWS, Azure, GCP, or private hybrid clouds) using containerization (Docker, Kubernetes) and Infrastructure as Code (IaC2. Consulting: Business, Technical Business

Defining purpose and business problem : Establishing the need for AI deployment

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