Designs and deploys end‑to‑end AI agent workflows, including RAG pipelines and prompt‑engineering layers, for production use. Integrates agents with databases, rule engines, and validation systems while optimizing scalability and accuracy.
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)
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