Job Summary
We are seeking a Principal AI Engineer with deep expertise in Multi-Agent Retrieval-Augmented Generation (RAG), large language models, and scalable AI systems. This role is responsible for architecting, developing, optimizing, and deploying advanced AI solutions that support complex enterprise workflows and large-scale information retrieval platforms. The ideal candidate will have strong Python programming skills, hands-on experience with AI/ML frameworks, and expertise in prompt engineering, model evaluation, and production-grade AI deployment.
Key Responsibilities
Design, develop, test, evaluate, and implement AI models with a focus on Retrieval-Augmented Generation (RAG) systems
Architect multi-agent AI systems capable of planning, tool usage, and coordinated workflow execution
Design and optimize RAG pipelines including hybrid retrieval, reranking, and context-window strategies
Fine-tune and evaluate language models for domain-specific reasoning, summarization, and information retrieval tasks
Develop prompt engineering frameworks, guardrails, and automated evaluation suites to improve AI reliability and performance
Build scalable machine learning services and APIs for production deployment in distributed environments
Collaborate with cross-functional teams to integrate AI solutions into enterprise applications and platforms
Optimize AI models for performance, scalability, cost efficiency, and reliability
Perform data preprocessing, feature engineering, and model evaluation activities
Troubleshoot and debug AI models, services, and applications within complex development environments
Document AI architectures, workflows, processes, and technical implementations
Conduct research and stay current with advancements in AI, machine learning, NLP, and generative AI technologies
Contribute to engineering best practices, code quality standards, and continuous improvement initiatives
Required Qualifications
Proven experience as an AI Engineer or similar role
Strong proficiency in Python programming
Deep expertise in Retrieval-Augmented Generation (RAG) models and architectures
Experience architecting multi-agent AI systems for complex workflows
Strong experience with prompt engineering and prompt testing methodologies
Experience designing automated evaluation frameworks and AI guardrails
Experience fine-tuning and evaluating language models
Strong experience building scalable ML services and APIs for production environments
Experience with machine learning frameworks such as TensorFlow or PyTorch
Knowledge of NLP techniques and modern AI architectures
Experience with data preprocessing, feature engineering, and model optimization
Strong analytical, troubleshooting, and problem-solving skills
Excellent communication and collaboration abilities
Experience working in distributed or large-scale environments
Preferred Qualifications
Experience with cloud platforms such as AWS, Azure, or Google Cloud
Experience deploying AI and ML models into production environments
Familiarity with Git or other version control systems
Experience optimizing AI systems for large-scale enterprise applications
Experience with distributed AI systems and scalable inference architectures