AI Developer

Role Description

We are seeking a highly skilled AI Developer with a strong backend and machine learning engineering background to design, train, optimize, and deploy LLM models in on-prem and offline environments. This role is deeply technical and hands-on.

You will work closely with our engineering and product teams to build end-to-end LLM pipelines — including:

• Data preprocessing
• Supervised fine-tuning
• Model quantization
• Evaluation
• RAG pipeline design
• Deployment using local or air-gapped infrastructure

If you enjoy working with cutting-edge open-source LLMs, building context-aware AI systems, and designing reliable backend pipelines, this role is for you.

Qualifications

• Strong experience in Python for backend and machine learning development
• Expertise with ML frameworks such as PyTorch or TensorFlow, along with scikit-learn and pandas
• Solid knowledge of Postgres or MySQL for data storage
• Experience with Docker and Git
• Hands-on experience with LLM training, fine-tuning, and optimization
• Experience with Hugging Face Transformers & Datasets
• Familiarity with XML/XSD and Office document parsing tools
• Experience deploying models with vLLM, TGI, or Ollama
• Understanding of quantization techniques such as GGUF, GPTQ, or AWQ
• Experience with GPU optimization and the CUDA stack
• Experience building solutions for offline, on-prem, and air-gapped environments
• Hands-on experience designing and implementing RAG pipelines, including embedding models, vector stores, and retrieval optimization strategies
• Experience building or integrating MCP (Model Context Protocol) servers to connect LLMs with external tools, APIs, and structured data sources
• Experience with advanced RAG techniques such as HyDE or multi-hop retrieval

Requirements

• Train and fine-tune LLMs using supervised fine-tuning (SFT)
• Work with open-source models such as LLaMA, Mistral, Qwen, and similar architectures
• Build LoRA / Q-LoRA pipelines for efficient fine-tuning
• Implement and optimize data preprocessing workflows, including tokenization and long-context handling
• Use and extend Hugging Face Transformers & Datasets for training and inference
• Parse and process structured and semi-structured data, including XML/XSD files
• Implement document parsing solutions for Office formats (python-docx, OpenXML)
• Design and implement end-to-end Retrieval-Augmented Generation (RAG) pipelines for document-grounded question answering and knowledge retrieval
• Build and maintain vector stores and embedding pipelines using tools such as FAISS, Chroma, Weaviate, or pgvector
• Optimize retrieval strategies including hybrid search, re-ranking, and chunking approaches tailored for domain-specific corpora
• Develop and maintain MCP (Model Context Protocol) server integrations to enable LLMs to interact dynamically with tools, APIs, and external data sources
• Design agentic workflows that leverage MCP to give models structured access to internal systems and context in a controlled, auditable manner
• Deploy, run, and maintain models fully offline and in air-gapped environments
• Perform model optimization and quantization (GGUF, GPTQ, AWQ, bitsandbytes)
• Build and maintain inference systems using frameworks like vLLM, TGI, and Ollama
• Optimize GPU usage (CUDA, cuDNN, VRAM-aware batching)
• Maintain local CI/CD pipelines for ML models without cloud dependencies
• Manage local model registries, versioning, and artifacts
• Ensure RAG and MCP components are fully operational in offline and restricted network environments
• Build backend services in Python for ML training and inference workflows
• Work with relational databases (Postgres/MySQL) and vector databases for RAG storage layers
• Use Docker and Git for reliable development and deployment pipelines
• Use Azure DevOps for CI/CD, including local runners when applicable

Benefits