Senior AI/ML Engineer - R01571454

Senior AI/ML Engineer

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)