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 Qualifications:

• 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