Job VC
Hybrid AI Engineer (LLM , Vision Language Models)
Technologies
Description
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.
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.