Senior AI/ML Engineer \n 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) \n