Lead AI Data Engineer

Job Description

A leading organization is seeking a Lead AI Data Engineer to design and deliver enterprise-scale AI solutions, data integration platforms, and intelligent automation capabilities. This position combines technical leadership with hands-on engineering responsibilities, requiring an individual who can architect complex systems, write production code, and guide engineering teams.

The ideal candidate has experience building AI-driven platforms, integrating enterprise applications, and developing scalable cloud-based solutions. Experience within healthcare, finance, or other highly regulated industries is strongly preferred.

Responsibilities

• Design and implement enterprise integration solutions across business-critical systems.
• Develop real-time data exchange and integration capabilities between enterprise platforms.
• Build, deploy, and maintain scalable Python-based microservices and APIs.
• Design and implement multi-agent AI systems and orchestration frameworks.
• Create autonomous and semi-autonomous workflows that improve operational efficiency.
• Develop and optimize enterprise data pipelines and workflow automation processes.
• Design data models that support AI applications and analytical workloads.
• Implement AI-powered solutions that streamline operations and business processes.
• Build and support event-driven architectures and distributed systems.
• Lead technical design decisions and contribute to architecture strategy.
• Mentor and support engineers while remaining actively involved in software development.
• Partner with technical and business stakeholders to deliver scalable solutions and modernization initiatives.

Required Experience and Skills

AI Engineering

• Experience building multi-agent AI systems
• Agent orchestration and workflow design
• LLM integration
• Autonomous workflow development
• Agentic AI solution development

Software Development

• Python
• SQL
• API development
• REST APIs
• Microservices architecture
• Enterprise application integration

Data Engineering

• ETL and ELT pipeline development
• Real-time data integration
• Workflow orchestration
• Data modeling
• Data platform design and optimization

Cloud and Infrastructure

• Google Cloud Platform (GCP)
• Kubernetes
• Composer
• BigQuery
• Experience deploying and supporting cloud-native applications

Enterprise Integration

• Event-driven architectures
• Enterprise systems integration
• Scalable integration design
• Real-time workflow connectivity

Leadership and Communication

• Proven experience as a hands-on technical lead
• Ability to mentor engineers while actively contributing code
• Strong communication and stakeholder management skills
• Strategic mindset with a focus on automation and modernization
• Experience driving engineering best practices and technical decision-making

Preferred Experience

• LangGraph
• LangChain
• Agentic AI frameworks
• MCP implementations
• Large-scale production AI deployments
• Healthcare, finance, insurance, or other highly regulated industry experience

FAQ

1. What does a Lead Front-End AI Engineer do?

A Lead Front-End AI Engineer designs and develops user interfaces for AI-powered applications while providing technical direction to frontend teams. The role combines modern frontend engineering with AI integration, focusing on intuitive user experiences, scalable architecture, and reliable interactions with intelligent services.

2. How are AI capabilities integrated into frontend applications?

AI features can be integrated through APIs and application services that provide capabilities such as conversational assistance, intelligent search, recommendations, content generation, and workflow automation. The engineer designs frontend experiences that present AI outputs clearly and allow users to interact with them effectively.

3. Which frontend technologies are commonly used in this role?

Modern environments may use React, Angular, Vue.js, TypeScript, JavaScript, HTML, and CSS. Engineers may also work with component libraries, state management tools, API clients, testing frameworks, and frontend observability platforms.

4. What role does TypeScript play in AI-enabled frontend development?

TypeScript provides stronger typing and development tooling for complex frontend applications. It helps teams build maintainable interfaces, define reliable data contracts for AI and backend APIs, and reduce errors as applications and feature sets grow.

5. How are conversational AI interfaces designed on the frontend?

The engineer creates interfaces for chat, prompts, streaming responses, conversation history, feedback, citations, tool execution states, and error handling. Good design also gives users clear visibility into system status and appropriate controls over AI-generated results.

6. What are important UX considerations for AI-powered interfaces?

AI interfaces should provide understandable feedback, responsive interactions, clear error states, and appropriate user control. Designers and engineers also need to consider accessibility, transparency, uncertainty, and how users review or correct AI-generated information.

7. How does a Lead Front-End AI Engineer improve application performance?

The engineer optimizes rendering, state management, network requests, bundle size, streaming behavior, and client-side resource usage. For AI applications, they may also optimize how model responses are streamed and displayed to reduce perceived latency.