Designs and deploys end‑to‑end AI agent workflows, including RAG pipelines and prompt‑engineering layers, for scalable production use. Collaborates with databases, rule engines, and validation tools to deliver reliable LLM‑based applications.
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)
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