AI Engineer

Huron is a global consultancy that collaborates with clients to drive strategic growth, ignite innovation and navigate constant change. Through a combination of strategy, expertise and creativity, we help clients accelerate operational, digital and cultural transformation, enabling the change they need to own their future.

Join our team as the expert you are now and create your future.

We are seeking an experienced AI/ML Engineer with expertise in both machine learning (ML) and Generative AI (GenAI) to join our Emerging Technology Practice. In this role, you will drive the development of cutting-edge AI models and solutions—from statistical modeling and predictive analytics to advanced generative and agentic applications.

• Design and develop AI-powered systems using both traditional ML and Generative AI techniques, including prompt engineering, fine-tuning, embeddings, and Retrieval-Augmented Generation (RAG) architectures
• Build intelligent applications and agentic systems that can perform tasks autonomously or interactively, using frameworks such as LangChain, LangGraph, AutoGen, CrewAI, or similar
• Architect and implement end-to-end RAG pipelines, including document ingestion, chunking strategies, embedding generation, vector indexing, hybrid search, and contextual response generation
• Develop modular, well-documented APIs and service components (e.g., using FastAPI or Flask) to enable model integration and consumption
• Design, build, and manage end-to-end AI pipelines, including data ingestion, transformation, validation, model training, inference, deployment, and knowledge retrieval workflows
• Leverage AWS and Azure AI/ML services (e.g., Bedrock, SageMaker, Azure OpenAI, Azure ML, Document Intelligence, vector databases, etc.) to accelerate development and deployment
• Implement observability, evaluation, and monitoring mechanisms to track model accuracy, drift, retrieval quality, grounding fidelity, reliability, and task-level reasoning
• Optimize RAG systems for latency, scalability, cost efficiency, and response quality in production environments
• Collaborate with software engineers, data scientists, solution architects, and business stakeholders to ensure seamless solution design and transition to production
• Support both rapid experimentation (POCs, pilots) and robust, scalable deployments, depending on the maturity of each use case
• Stay up to date with emerging trends in Generative AI, AI agents, orchestration frameworks, RAG evaluation techniques, and continuous improvement methodologies