Machine Learning Champion (Remote)

at  Atacana

REQUIRED TECHNICAL SKILLS & EXPERIENCE

• 3+ years of experience in machine learning engineering with focus on NLP systems
• Extensive experience with RAG components and architectures:
• Query optimization and rewriting
• Vector databases
• Document chunking and embedding strategies
• Reranking methods
• Prompt engineering and LLM integration
• Hands-on experience with agent frameworks:
• LangChain & LangGraph
• Agent communication protocols
• Workflow orchestration
• Proficiency in:
• Python (advanced)
• Vector similarity search
• Semantic embeddings
• Document processing pipelines

PREFERRED QUALIFICATIONS

• Fast learner and curious about the latest technologies
• Experience implementing RAG systems serving high query volumes in production
• Experience in information retrieval, semantic search, or related areas in technical fields
• Experience with multimodal retrieval and generation

ABOUT THE ROLE

We are seeking an experienced Machine Learning Engineer with deep expertise in building production-grade Retrieval-Augmented Generation (RAG) systems and AI agents. The ideal candidate will have a strong foundation in machine learning, natural language processing, and practical experience implementing end-to-end RAG pipelines and agent orchestration.

KEY RESPONSIBILITIES

• Design and implement sophisticated RAG architectures incorporating query classification, retrieval optimization, reranking, repacking, and summarization components
• Develop and optimize chunking strategies for document processing, balancing context preservation with retrieval efficiency
• Research and implement advanced retrieval methods combining sparse (BM25) and dense retrieval with hybrid search techniques
• Build and optimize vector databases for efficient similarity search at scale
• Create evaluation frameworks to measure RAG system performance across multiple dimensions (faithfulness, relevancy, retrieval accuracy)
• Design and deploy multi-agent systems with effective orchestration patterns
• Develop multimodal RAG capabilities integrating text, images, and other modalities
• Lead technical design reviews and mentor junior engineers in RAG/agent implementation best practices