Lead AI Engineer
J
JobgetherLegal EdTech
Based in the United StatesFull-TimeLead
Salary140,000 - 165,000 USD per year
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Job Details
- Experience
- Minimum of 7 years
- Required Skills
- PythonCloud ComputingFull Stack DevelopmentAzureRESTful APIsLLM
Requirements
- Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.
- Minimum of 7 years of experience building production software, backend systems, data platforms, or AI-driven applications.
- Strong proficiency in Python, including experience developing APIs, integrations, automation workflows, and scalable backend services.
- Hands-on experience with Large Language Models (LLMs), prompt engineering, AI agents, and production AI application development.
- Practical expertise implementing RAG architectures, vector databases, semantic search, or advanced information retrieval systems.
- Full-stack engineering capabilities with the ability to independently design, develop, test, and deploy solutions.
- Experience integrating enterprise platforms, cloud services, and third-party APIs.
- Familiarity with cloud ecosystems such as Microsoft Azure, including AI and cognitive services, is highly desirable.
- Experience with workflow automation platforms such as Zapier, Power Automate, n8n, or similar tools is considered an asset.
- Exposure to voice AI technologies, including speech-to-text and text-to-speech solutions, is preferred.
- Experience working within regulated industries, education technology, legal technology, or startup environments is advantageous.
- Excellent problem-solving, communication, and stakeholder management skills, with the ability to explain technical concepts to non-technical audiences.
Responsibilities
- Design, build, and deploy AI-powered applications, intelligent agents, and automation solutions to enhance products and business operations.
- Develop and maintain scalable backend services, APIs, integrations, and cloud-based systems using Python and modern engineering practices.
- Implement retrieval-augmented generation (RAG) architectures, LLM orchestration frameworks, vector search solutions, and AI evaluation methodologies.
- Lead the creation of AI-driven simulation experiences, including conversational and voice-enabled learning applications.
- Identify opportunities to integrate AI capabilities into existing products and rapidly deliver high-impact features.
- Build and optimize data pipelines and integrations across enterprise platforms, business applications, and third-party services.
- Collaborate closely with product, engineering, and business stakeholders to define requirements and translate ideas into scalable solutions.
- Provide technical leadership on architecture decisions, engineering standards, AI platform strategy, and vendor evaluations.
- Support and enable non-technical teams by promoting best practices, training, and guidance around AI tools and automation capabilities.
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