AI Engineer

Fractal is a global AI and technology company that helps large enterprises build modern, data‑driven products and solutions. We work with leading organizations across multiple industries, to deliver high‑quality engineering and cloud‑native platforms.

We are now looking for a Hybrid AI Engineer to join a high‑impact AI initiative for one of our global clients. As a Hybrid AI Engineer, you will work on developing applied LLM/VLM‑driven features across the full model lifecycle. You will design and implement data pipelines for model training, build and optimize RAG systems and contribute to fine‑tuning workflows (LoRA/SFT). This is a short‑term (4–6 months), contract‑based, hourly role focused on building core components of an internal AI platform.

Key Responsibilities

• Data Engineering & Curation: Build and manage data pipelines, including dataset preparation, filtering, deduplication, and preprocessing for ML workflows
• Model Training & Fine-Tuning: Train and fine-tune models using techniques such as LoRA, SFT, and DPO for LLM-based systems
• AI Systems Development: Develop RAG pipelines, evaluation frameworks, and benchmarking systems for applied AI use cases
• Deployment & Optimization: Deploy models using SageMaker, EKS, vLLM, or NIM and optimize for latency, throughput, and cost
• Multimodal Workflow Support: Work with document and image-based datasets, supporting OCR, document AI, and multimodal training/inference workflows

Must Have Skills

• ML Engineering & Model Training: Strong Python skills with hands-on experience in PyTorch/Hugging Face and LLM fine-tuning techniques (LoRA, SFT, DPO)
• Data Pipelines & Dataset Engineering: Experience building and managing data pipelines, including dataset preparation, filtering, and preprocessing
• LLM Systems & Evaluation: Practical understanding of RAG systems, model evaluation, benchmarking, and applied AI workflows
• Applied AI Problem Solving: Ability to design, debug, and optimize end-to-end ML systems across data, training, and inference stages
• Multimodal Data & VLM Awareness: Familiarity with image-text/document datasets, OCR/document AI workflows, and basic multimodal concepts
• English level: Upper‑Intermediate (B2) or higher — must

Years of Experience

• 2–5 years of ML / data engineering experience
• Hands-on experience (6–24 months) with LLMs preferred

This is a great opportunity to work alongside a team of skilled professionals while enjoying full flexibility, autonomy, and a fast, no‑bureaucracy environment. If you’re looking for a project where your expertise truly shapes the outcome — we’d be glad to hear from you.