Sr. AI Engineer Consultant (Hybrid-In Office)

About the role

Join Centric Consulting – A Culture You’ll Love

Centric Consulting is a unique consulting firm built on three fundamental principles: Enjoy the people you work with, have fun, and do great work. These principles define our consulting model and have crafted one of the most vibrant cultures in the consulting industry – celebrating individuals, collaboration, and lifelong friendships.

Responsibilities

• Copilot Optimization: Analyze and refactor Copilot Studio agents to eliminate redundancies, improve routing efficiency, and streamline topic structures for better maintainability.
• Generative AI Modernization: Transition static, rule-based topics into grounded generative experiences and replace legacy ingestion methods with modern Azure AI approaches.
• Advanced RAG Implementation: Enhance solution accuracy by integrating Azure AI Foundry capabilities, including Cognitive Search with vector embeddings and custom RAG endpoints.
• Sophisticated AI Features: Implement high-level capabilities like multi-step reasoning, context persistence, summarization, and function calling for complex user interactions.
• End-to-End MLOps: Design and manage scalable AI/ML pipelines, covering everything from model fine-tuning and versioning to CI/CD, monitoring, and security.
• Architectural Leadership: Serve as the technical lead to design scalable architectures, guiding best practices and ensuring internal teams can maintain the solution post-handoff.
• Operational Enablement: Develop infrastructure-as-code for resilient workloads and produce comprehensive documentation, including troubleshooting guides and operational playbooks.

Requirements

Experience: 8+ years in software architecture or cloud engineering, with at least 3+ years specifically focused on production-grade AI/ML solutions.

AI Ecosystem: Strong hands-on experience with Microsoft Copilot Studio and Azure AI Foundry (formerly Azure AI Studio).

Technical Proficiency: Expert-level Python for AI/ML development and automation; additional experience in C#, .NET, or Java is preferred.

AI Specialization: Solid understanding of Generative AI, prompt engineering, RAG architectures, embeddings, and vector databases.

MLOps & DevOps: Demonstrated experience with model lifecycle management, CI/CD pipelines, and designing resilient, serverless AI workloads.

Problem Solving: Proven ability to troubleshoot, refactor, and optimize existing AI solutions to meet 99.8% accuracy targets.

Qualifications

Ability to lead architectural discussions with executive stakeholders and translate business needs into technical AI requirements.