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AI Forward Deployed Engineer 52-Week Mastery
Published 9/2026
MP4 | Video: h264, 3840x2160 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 42h 45m | Size: 40.37 GB
Master production AI, RAG, agents, cloud deployment, enterprise delivery, and client-facing engineering in 52 weeks.
What you'll learn
Translate customer problems into clear AI use cases, requirements, success metrics, and implementation plans.
Design scalable enterprise AI architectures using models, APIs, databases, data pipelines, cloud services, and user interfaces.
Build maintainable AI applications with Python, FastAPI, REST APIs, SQL, testing, logging, and error handling.
Develop production-ready Generative AI and large language model applications with prompts, structured outputs, sessions, and guardrails.
Build advanced Retrieval-Augmented Generation systems using document ingestion, chunking, embeddings, vector databases, hybrid search, reranking, and citations.
Create AI agents that use tools, APIs, databases, memory, state, human approvals, and failure-recovery strategies.
Design multi-agent systems using routing, supervisor-worker patterns, specialized agents, shared context, and Model Context Protocol integrations.
Package and deploy AI applications using Docker, cloud platforms, CI/CD pipelines, secrets management, scaling, and rollback strategies.
Evaluate and monitor AI systems using test datasets, observability, tracing, human review, cost tracking, and performance metrics.
Implement AI security, privacy, responsible AI, audit logging, governance, and human-oversight controls.
Conduct customer discovery sessions, manage stakeholders, present technical trade-offs, and communicate measurable business value.
Complete a production-style enterprise AI capstone project and create a professional AI engineering portfolio.
Requirements
Basic programming knowledge is helpful, but advanced AI or machine learning experience is not required.
Familiarity with Python fundamentals is recommended, although essential concepts are reviewed during the course.
A computer capable of running a code editor, web browser, Python, Git, and Docker is recommended.
Internet access is required for software packages, AI model providers, cloud platforms, documentation, and deployment exercises.
A code editor such as Visual Studio Code or another preferred development environment.
Free or trial accounts may be needed for selected cloud services, AI model providers, source-control platforms, or vector databases.
Basic familiarity with APIs, databases, Git, or cloud computing is useful but not mandatory.
A willingness to troubleshoot, experiment, review logs, improve failed solutions, and complete hands-on projects.
No previous consulting, solution architecture, data science, or Forward Deployed Engineering experience is required.
Many exercises can be completed locally or with free service tiers, so paid cloud resources are not required for every project.
Description
This course contains the use of artificial intelligence.
Become anAI Forward Deployed Engineer who can transform ambitious AI ideas into secure, scalable, measurable, production-ready enterprise solutions.
AI Forward Deployed Engineer: 52-Week Mastery is a comprehensive, hands-on program designed for developers, AI engineers, software engineers, solution architects, technical consultants, and technology professionals who want to masterproduction AI engineering, Generative AI, Retrieval-Augmented Generation (RAG), AI agents, cloud deployment, enterprise integration, and customer-facing technical delivery.
Over 52 weeks, you will learn the completeAI solution delivery lifecycle, from customer discovery and requirements gathering to architecture, development, testing, deployment, monitoring, governance, and business value measurement. You will learn how Forward Deployed Engineers identify high-value business problems, define technical requirements, design practical architectures, build rapid prototypes, integrate enterprise systems, and move AI applications into production.
The course begins with essential engineering skills includingPython, FastAPI, REST APIs, SQL, Git, backend development, testing, logging, data pipelines, and API integration. You will build reliable AI services that connect models with databases, documents, enterprise applications, APIs, and business workflows.
You will then exploremachine learning and Generative AI, including large language models, prompt engineering, structured outputs, context management, model selection, latency optimization, token management, and AI cost optimization. You will build secure conversational AI applications with guardrails, user sessions, feedback systems, and human approval workflows.
A major focus of the program isRetrieval-Augmented Generation (RAG). You will work with embeddings, vector databases, semantic search, hybrid search, metadata filtering, reranking, document ingestion, citations, hallucination reduction, and RAG evaluation. You will build enterprise knowledge assistants that generate grounded answers using trusted organizational information.
You will also designAI agents and multi-agent systems capable of using tools, calling APIs, querying databases, maintaining memory and state, recovering from failures, and escalating sensitive actions to humans. Advanced topics include agent routing, supervisor-worker architectures, multi-agent orchestration, shared context, tool calling, and theModel Context Protocol (MCP).
Production engineering modules coverDocker, CI/CD pipelines, cloud deployment, secrets management, scaling, observability, tracing, monitoring, rollback strategies, incident response, reliability, and AI cost tracking. You will learn how to deploy and operate AI applications safely in real enterprise environments.
The program also coversAI security and governance, including prompt injection defense, access control, privacy, responsible AI, audit logging, data protection, human oversight, and enterprise AI risk management.
Throughout the course, you will complete hands-on exercises, architecture challenges, customer-style scenarios, and portfolio projects. You will practice customer discovery, stakeholder communication, solution demonstrations, technical trade-off discussions, and business-value presentations.
The 52-week journey concludes with a completeenterprise AI capstone project that you will design, build, deploy, document, evaluate, and present.
By the end of the program, you will have practical experience acrossAI engineering, RAG, Generative AI, AI agents, FastAPI, APIs, Docker, cloud deployment, enterprise architecture, AI security, governance, technical consulting, and customer-facing delivery-the core skills required for modernAI Forward Deployed Engineer roles.
No advanced AI background is required. The curriculum progresses from foundational software engineering to production-grade enterprise AI delivery, giving you a structured roadmap and portfolio evidence to demonstrate your ability to solve real-world AI problems.
Who this course is for
Software developers who want to transition into AI engineering, applied AI, or Forward Deployed Engineering roles.
AI and machine learning engineers who want stronger skills in enterprise integration, deployment, customer discovery, and production delivery.
Solution architects and cloud engineers who need to design secure, scalable, and observable Generative AI platforms.
Data engineers and analysts who want to build RAG pipelines, AI assistants, intelligent workflows, and agent-powered applications.
Technical consultants and implementation specialists who work directly with customers and convert business requirements into deployed AI solutions.
Product managers and technical product professionals who want to understand AI architecture, feasibility, delivery risks, and evaluation.
DevOps, platform, and MLOps engineers who want to support containerized AI services, CI/CD pipelines, monitoring, governance, and production operations.
Freelancers, founders, and technical entrepreneurs who want to deliver enterprise-grade AI solutions or launch AI-powered products.
Career changers with programming experience who want a structured 52-week path toward an AI engineering portfolio.
Students and recent graduates seeking practical experience beyond theoretical machine learning or prompt-engineering courses.
Anyone who wants to build production-ready AI systems rather than stopping at notebooks, basic chatbots, or proof-of-concept demonstrations.
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