Lead AI Engineer
Responsibilities:
Job Information
The ideal candidate will have deep expertise in data engineering, agentic AI systems, large language models (LLMs), and Model Context Protocol (MCP). The preferred candidate will bring deep experience with AWS and Snowflake services, including a strong understanding of security best practices for cloud-based AI and data solutions. This role requires a hands-on leader who can design scalable, secure, and innovative data pipelines and AI solutions that deliver business value.
Key Job Functions
• Solution Engineering: Lead the design, development, and deployment of enterprise-scale data and AI solutions, ensuring alignment with business objectives and technical best practices.
• LLM and Agentic AI: Architect, implement, and optimize large language models and agentic AI workflows for business automation and decision support.
• Framework Expertise: Design and deploy AI solutions using leading frameworks such as LangChain, LangGraph, and n8n for scalable agent orchestration, workflow automation, and integration with business systems
• Model Context Protocol (MCP): Develop, integrate, and manage MCP-based solutions to enhance model interpretability, context management, and deployment at scale.
• Cloud and Data Engineering: Leverage AWS and Snowflake to build scalable, secure, and efficient data pipelines for structured and unstructured data.
• Collaboration: Partner with cross-functional teams, including other technology, business, risk, legal, and compliance stakeholders, to deliver integrated solutions.
• Innovation: Stay current with emerging technologies and industry trends in AI, data engineering, and cloud computing, driving continuous improvement and innovation.
• Governance and Compliance: Ensure all solutions meet regulatory, security, and compliance requirements relevant to the financial services industry.
• Mentorship: Provide technical leadership and mentorship to junior team members.
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