AI - Engineer

About the role

We're building Voxy, a deeply personalised AI companion that speaks, listens, and feels real. This isn't a ChatGPT wrapper. We train and fine-tune our own models, run our own inference stacks, and obsess over every millisecond of latency and every token of quality.

What you'll do

• If you've fine-tuned a model, shipped it to production, and then debugged why it degraded three weeks later, we want to talk.
• What You'll Own
• LLM Fine-tuning for Companion AI, SFT, RLHF, DPO pipelines on open-source base models (Gemma, LLaMA, Mistral family, etc ) to build Voxy's conversational personality engine
• Efficient Training, PEFT methods including LoRA, QLoRA, adapter layers; balancing quality vs. compute budget
• Text-to-Speech & Voice, Fine-tune TTS models (XTTS, Tortoise, StyleTTS2 or similar); work on prosody, emotion, and low-latency streaming voice output
• Text-to-Image, Fine-tune diffusion models (Stable Diffusion, FLUX) using DreamBooth, LoRA, ControlNet for companion avatar generation
• Quantisation & Inference, GPTQ, AWQ, GGUF, bitsandbytes; optimise models for GPU and edge targets without killing quality
• Evaluation Systems, Build robust eval harnesses: automated metrics, human eval pipelines, regression detection, and model behaviour monitoring

What they're looking for

• 3, 6 years of experience, with at least 2 years directly in ML/AI model work (not just ML infra or MLOps)
• Hands-on fine-tuning experience on transformer-based LLMs, not just running training scripts, but understanding why hyperparameters matter
• Deep understanding of attention mechanisms, multi-head attention, RoPE, GQA, Flash Attention, can reason about tradeoffs, not just use them
• Practical experience with PEFT: LoRA rank selection, target modules, merging strategies, catastrophic forgetting mitigation
• Strong grasp of quantisation, INT4/INT8, calibration, quality-compute tradeoffs across GPTQ/AWQ/GGUF
• Experience with model evaluation, BLEU/ROUGE are a floor, not a ceiling; you've built task-specific evals
• Data-first mindset, can write complex SQL, has built data pipelines for training, understands data quality deeply
• Strong DSA fundamentals, can pass a coding round at a top product company; writes efficient, production-quality Python

Nice to have

• Prior work on voice models, TTS fine-tuning, voice cloning, vocoder pipelines (HiFi-GAN, BigVGAN), streaming inference
• Experience with diffusion models, training dynamics, classifier-free guidance, LoRA for personalisation
• Familiarity with multi-modal architectures, how vision encoders interface with LLMs (LLaVA, Idefics, etc.)
• Contributed to open-source ML projects or published papers/technical blogs with real traction
• Experience with distributed training, DDP, FSDP, DeepSpeed ZeRO stages
• Proven Track Record, This Is Non-Negotiable

About Eloelo Group (StoryTV)