Data Scientist - R01571454

Senior AI/ML Engineer

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)