Machine Learning Engineer - Applied AI (Fully Remote)

Job description for Machine Learning Engineer - Applied AI (Fully Remote) at Elitez Group

Key Responsibilities

End-to-End System Execution

Own the full ML lifecycle: data preparation, training workflows, evaluation, inference architecture, and deployment for long-horizon AI capabilities.

Model Fine-Tuning & Adaptation

Adapt and optimize modern LLMs and foundation models using techniques like LoRA, QLoRA, SFT, DPO, and model distillation.

Inference Optimization

Architect and operate low-latency, cost-effective inference pipelines, managing GPU memory efficiency and scaling policies.

Evaluation & Robustness

Design and implement rigorous evaluation and benchmarking frameworks covering model quality, safety, bias, and edge-case failure modes using real production signals.

Data Systems Engineering

Build and maintain pipelines for high-quality synthetic and real-world training/evaluation datasets.

Technical Leadership & Integration

Collaborate closely with product, backend, and frontend teams to integrate ML capabilities cleanly into products while mentoring junior engineers through code reviews and technical guidance.

Key Requirements

Production ML Experience

Proven track record of building, fine-tuning, and shipping production ML systems used by real users.

Deep Model Knowledge

Strong understanding of how modern transformer models and LLMs behave—and misbehave—in production environments.

Core Tech Stack

Expert proficiency in Python and deep learning frameworks (PyTorch or JAX).

Adaptation & Inference Mastery

Hands-on experience with model adaptation (LoRA/SFT/DPO) and GPU inference systems/optimization.