Senior Ai Engineer

Responsibilities:

Design and build production-grade RAG (Retrieval-Augmented Generation) pipelines using vector databases, hybrid retrieval strategies, and reranking layers Fine-tune and evaluate large language models for domain-specific use cases across client and internal projects Build and maintain AI-powered automation workflows using n8n, integrating LLMs, APIs, and external data sources Develop and deploy AI features into web and mobile products in collaboration with the engineering team Work with frameworks including LangChain, TensorFlow, PyTorch, or equivalent to build scalable AI systems Own model evaluation, performance monitoring, and iterative improvement of deployed AI systems Contribute to the AI architecture and tooling decisions across LAKCHE's product suite Document AI systems clearly and maintain reproducible, version-controlled pipelines

Requirements:

Minimum 5 years of professional software or AI engineering experience Python as primary language  strong, demonstrable proficiency is non-negotiable Proven hands-on experience building RAG pipelines in production environments Hands-on experience with LLM fine-tuning  dataset preparation, training, evaluation, and deployment Proficiency with at least one major AI framework: LangChain, TensorFlow, PyTorch, or equivalent Practical, production-level experience with n8n for AI workflow automation Strong understanding of vector databases Pinecone, Weaviate, ChromaDB, or similar Solid grasp of prompt engineering, context window management, and LLM orchestration patterns A strong AI project portfolio with verifiable references is mandatory applications without one will not be considered

Preferred Requirements:

Experience with multimodal models (vision, audio, or document AI) Familiarity with cloud AI services  AWS Bedrock, Google Vertex AI, or Azure OpenAI Exposure to MLOps practices model versioning, CI/CD for ML, monitoring Experience with FastAPI or similar for serving AI models via APIs Knowledge of data pipelines and ETL for AI training datasets Familiarity with LLM evaluation frameworks Ragas, TruLens, or similar

Benefits:

Hybrid work arrangement a mix of on-site and remote flexibility Competitive compensation based on experience and demonstrable impact Work on live, cutting-edge AI products with real users and real stakes Direct collaboration with the founding leadership and the Lead Developer Clear growth path into an AI Lead or Head of AI function as LAKCHE scales A team culture that takes AI seriously we build with it, not just talk about it