Senior AI/ML Engineer - R01572335

Senior AI/ML Engineer

Job requirements: Experience Range: 2 to 4 years of experience, including at least 2 years specifically focused on developing 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 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 Required Skills: 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 Preferred Skills: 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 Desired Qualifications: 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)