Senior AI/ML Engineer - R01572335

Job requirements

• Design and build end-to-end AI agent workflows, from initial prompt design through production deployment, ensuring scalable and reliable solutions
• Develop and optimize Retrieval-Augmented Generation (RAG) pipelines by implementing chunking strategies, embedding models, retrieval ranking, and context window management for precise information retrieval
• Build and iterate on prompt engineering layers, systematically testing and refining prompts and chain-of-thought strategies to deliver consistent outputs across diverse inputs
• Implement tool orchestration within agent workflows, integrating agents with databases, rule engines, validation systems, and formatting tools for seamless operation
• Establish automated quality checks and validation layers to proactively identify and resolve issues before outputs reach human reviewers
• Collaborate with Data Scientists to instrument solutions for measurement, developing evaluation frameworks and tracking solution performance against defined targets
• Deploy, monitor, and maintain AI/ML solutions in production environments, ensuring reliability, scalability, and robust error handling
• Design and implement feedback loops to capture expert review data and translate it into measurable improvements in agent performance

• Advanced proficiency in Python
• Hands-on experience with LLM frameworks such as LangChain or LlamaIndex
• Expertise in prompt engineering for systematic testing and iteration
• Deep understanding of RAG architectures including embedding models, vector stores, retrieval strategies, and re-ranking
• Experience building multi-step agent workflows with tool use and branching logic
• Experience deploying and maintaining AI/ML solutions in production environments
• Experience with data pipeline development for feeding AI systems

• Experience with multi-agent orchestration frameworks
• Background in content generation, translation, or document processing solutions
• Familiarity with feedback loops, RLHF, or reward model training
• Knowledge of multi-modal AI systems including voice-to-text, document understanding, and image analysis
• Experience with evaluation frameworks for generative AI and automated scoring
• Experience with LLM cost optimization strategies such as model routing, caching, and prompt compression

• Bachelor's degree in Computer Science, Data Science, Information Technology, Statistics, 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 engineering or generative AI (e.g., DeepLearning.AI Generative AI with LLMs)