À propos du poste

Senior AI/ML Engineer Job requirements Experience Range: - 2–4 years of experience, including at least 2 years specifically building LLM-based applications, RAG systems, or AI agent workflows Key Responsibilities: - Design and build end-to-end AI agent workflows, from initial prompt design through production deployment, ensuring scalable and robust solutions - Develop and optimize Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, embedding models, retrieval ranking, and context window management to maximize information accuracy and retrieval efficiency - Build and systematically iterate on prompt engineering layers, testing and refining prompts and chain-of-thought strategies to achieve consistent, high-quality outputs across diverse inputs - Implement tool orchestration within agent workflows by integrating agents with databases, rule engines, validation systems, and formatting tools to automate complex tasks - Establish automated quality checks and validation layers to proactively catch issues and ensure high output reliability before human review - Instrument solutions for measurement, collaborating with data scientists to develop evaluation frameworks and track solution performance against defined targets - Deploy and maintain AI/ML solutions in production environments, focusing on reliability, monitoring, and edge case handling - Design and implement feedback loops that capture expert review data and translate it into measurable improvements in agent performance Required Skills: - Advanced proficiency in Python - Hands-on experience with LLM frameworks such as LangChain, LlamaIndex, or similar - Expertise in prompt engineering and systematic prompt testing - Deep understanding of RAG architectures, including embedding models, vector stores, retrieval strategies, and re-ranking - Experience building multi-step agent workflows with tool use, branching logic, and robust error handling - Experience with production deployment and monitoring of AI/ML solutions - Experience with data pipeline tools and frameworks (KubeFlow, BentoML, Great Expectations, Evidently AI) Preferred Skills: - Experience with multi-agent orchestration frameworks - Background in content generation, translation, or document processing solutions - Familiarity with fine-tuning LLMs or training reward models - Experience implementing feedback loops or RLHF mechanisms - Expertise in LLM cost optimization strategies such as model routing, caching, and prompt compression - Experience with multi-modal AI systems including voice-to-text, document understanding, and image analysis - Experience with evaluation frameworks for generative AI, including automated scoring and human evaluation protocols Desired Qualifications: - Bachelor's degree in Computer Science, Data Science, Information Technology, or a closely related discipline - Certification in Machine Learning or Artificial Intelligence (e.g., TensorFlow Developer Certificate, AWS Certified Machine Learning Specialty) - Certification in LLM or generative AI technologies (e.g., OpenAI Certified Engineer, Hugging Face Certified AI Practitioner)