Senior AI/ML Engineer \n 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) \n