Generative AI Engineer — Inference Phase

🎨

Inference Specialization

Generative AI Engineer

Generative AI engineers build creative AI systems powering content generation, design tools, synthetic media, game asset pipelines, and marketing automation. The intersection of cutting-edge model research and product engineering. +135% YoY demand.

Days

Projects

$180K

Median

↑135%

YoY Demand

$130K–$240K

Salary Range

Week 1: Foundation Model Architecture Deep Dives · Days 1–8  

Understand the architectures behind modern text, image, and audio generation — not just API calls, but the engineering underneath.

D1–3

LLM architecture internals: GPT, Llama, and Mistral

Decoder-only Transformers, RoPE positional encoding, GQA, KV cache mechanics, SWA. Architecture decisions explained

↗ Pretraining LLMs — DeepLearning.AI (free)

D4–5

Diffusion model architecture: DDPM, DDIM, LDM, Flow Matching

Score matching, classifier-free guidance, latent diffusion, rectified flow as used in Stable Diffusion 3

↗ HuggingFace Diffusion Models Course (free)

D6–7

Audio generation: MusicGen, Suno API, and AudioCraft

Text-to-music generation, text-to-sound effects, AudioCraft architecture and inference optimisation

↗ HuggingFace Audio Course (free)

🏗 Project — Day D8: Custom Text-to-Image Generation Pipeline 

· SDXL pipeline with negative prompts and sampler controls

· ControlNet conditioning on pose and depth maps

· Batch generation for a product catalogue with 50 images

· CLIP score evaluation report comparing prompt adherence across configs

Week 2: Fine-Tuning, LoRA, and Custom Model Training · Days 9–24  

Train custom generative models on your own data: brand visual styles, custom text voice, and cloned speech synthesis.

D9–12

DreamBooth, LoRA, and Textual Inversion for image models

Fine-tune Stable Diffusion on custom visual concepts. Three techniques compared: DreamBooth vs LoRA vs TI

↗ HuggingFace DreamBooth training guide (free)

D13–16

RLHF and Direct Preference Optimisation for generative models

Human preference alignment for text and image generation. DPO applied to diffusion model aesthetic preferences

↗ RLHF — DeepLearning.AI (free)

D17–20

Voice cloning and custom TTS: XTTS2 and StyleTTS2

Zero-shot voice cloning with 3 seconds of reference audio, custom TTS fine-tuning, voice style transfer

↗ Coqui TTS and XTTS2 documentation (free)

D21–23

Video generation: AnimateDiff, CogVideoX, and Wan-2.1

Text-to-video, image-to-video, video editing. Temporal consistency, motion quality evaluation methods

↗ HuggingFace AnimateDiff documentation (free)

🏗 Project — Day D24: Custom Brand Voice and Visual Style System 

· Fine-tuned SDXL LoRA capturing a specific visual brand style

· Custom TTS voice fine-tuned with XTTS2 from 5 minutes of audio

· Gradio UI for brand-consistent content generation at scale

· Quality evaluation: FID score, CLIP alignment score, and MOS for voice

Week 3: Production Systems, Safety, and Multimodal Generation · Days 25–40  

Scale generative AI to production with inference optimisation, content safety systems, C2PA provenance, and a full capstone product.

D25–28

Inference optimisation: step distillation, LCM, and quantisation

Quantising diffusion models to INT8, step distillation with LCM-LoRA, SDXL Turbo for 4x faster generation

↗ HuggingFace distilled Stable Diffusion guide (free)

D29–32

Content safety: NSFW detection, watermarking, and C2PA provenance

C2PA content credentials standard, invisible watermarking, NSFW classifiers, synthetic media detection tools

↗ C2PA content authenticity standard documentation (free)

D33–36

GenAI product architecture patterns

Editor, co-pilot, and autonomous generator product archetypes. UX patterns, latency budgets, human-in-the-loop design

↗ Building AI Products — fast.ai Part 2 (free)

D37–39

Multimodal coherent generation: image-text stories and video narratives

Generating multi-scene content with consistent characters and style across frames. Story-to-video pipelines

↗ DeepLearning.AI multimodal short courses collection (free)

🏗 Project — Day D40: Generative AI Product — Full-Stack Capstone 

· Production GenAI application combining text, image, and audio generation

· Custom fine-tuned model component (at least one modality)

· Content safety filter layer with NSFW detection

· Cost optimisation achieving under $0.01 per generation request

· Public deployment with usage analytics dashboard