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.

• 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

• 4 to 6 years of hands-on experience in AI/ML development or intelligent application engineering
• Strong proficiency in Python and familiarity with modern ML/AI libraries (e.g., scikit-learn, PyTorch, TensorFlow, OpenAI APIs)
• Hands-on experience designing and deploying RAG-based systems, including embeddings, vector databases (e.g., FAISS, Pinecone, Chroma, Azure AI Search), and retrieval optimization techniques
• Experience building APIs or backend services using FastAPI, Flask, or equivalent frameworks
• Exposure to agent frameworks (e.g., LangGraph, AutoGen) and multi-agent orchestration patterns
• Experience with orchestration standards or tools such as MCP or agent routing protocols is a plus
• Familiarity with cloud platforms (AWS, Azure) and containerization tools (Docker)
• Hands-on experience with cloud-based ML platforms such as Azure Machine Learning, AWS SageMaker, or Google AI/ML services
• Experience with MLOps pipelines, CI/CD for model delivery, RAG evaluation frameworks (e.g., RAGAS), or model monitoring is a plus
• Knowledge of AI system evaluation, observability, prompt performance testing, and grounding validation is a plus
• Ability to work on cross-functional teams, balance multiple projects, and communicate effectively with technical and non-technical audiences
• Cloud certifications in AI/ML services (Azure, AWS, or Google Cloud) are a plus
• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field

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