Senior AI Engineer

Job Description
Our client is expanding the AI capabilities of its enterprise data platform built on Databricks and is seeking an experienced Senior AI Engineer to drive the development, deployment, and optimization of advanced machine learning and generative AI solutions.
The successful candidate will possess end-to-end expertise across the complete AI lifecycle, including data engineering, feature development, model training, deployment, monitoring, and continuous optimization. This role requires a strong understanding of balancing technical innovation with operational efficiency, ensuring AI solutions deliver measurable business value while maintaining cost-effective development, deployment, and runtime operations.
The position offers an opportunity to work with cutting-edge AI technologies, large-scale cloud platforms, and modern Generative AI architectures, including Retrieval-Augmented Generation (RAG), AI agents, copilots, and intelligent automation solutions.
Key Responsibilities
Design, develop, deploy, and maintain production-grade machine learning and AI solutions across the entire model lifecycle.
Build scalable AI architectures leveraging Azure, Databricks, and modern cloud-native technologies.
Develop and optimize Retrieval-Augmented Generation (RAG) solutions to enhance knowledge retrieval and intelligent content generation.
Create and manage embedding pipelines using OpenAI models, Databricks capabilities, and open-source alternatives.
Design and implement AI agent frameworks, including tool integration, orchestration, routing, memory management, and multi-agent collaboration.
Evaluate and select appropriate AI models by considering performance, scalability, business requirements, and cost efficiency.
Develop prompt engineering strategies, system prompts, and conversational workflows to improve AI solution effectiveness.
Define and implement monitoring, governance, and performance optimization practices for deployed AI systems.
Collaborate with cross-functional teams to align AI initiatives with business objectives and platform capabilities.
Assess architectural approaches, including RAG, fine-tuning, agent-based systems, and hybrid AI solutions.
Implement efficient data chunking strategies and retrieval mechanisms to improve model accuracy and response quality.
Support the development of AI copilots and intelligent assistant solutions for enterprise use cases.
Contribute to the integration and orchestration of multiple AI agents and intelligent systems.
Promote best practices in machine learning engineering, AI operations, and cloud-native development.

Required Experience
Minimum 5 years of experience developing, deploying, and maintaining production-grade machine learning models and AI solutions.
Proven experience across the complete machine learning lifecycle, from data preparation and feature engineering to deployment and monitoring.
Strong understanding of operational cost optimization and business value realization within AI and cloud environments.

Education

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