Lead Agentic AI Engineer @ Remote Job
Hello,
Hope you are doing well,
Job Title: Lead Agentic AI Engineer
Location: Remote
Role Overview
As an Lead Agentic AI Engineer you will design, build, and deploy autonomous AI systems. You will empower GM’s global teams by engineering intelligent agents capable of complex reasoning, multi-agent orchestration, and autonomous task execution. Bridges advanced LLM capabilities with complex, large-scale automotive data environments.
Key Responsibilities
· Agent Architecture: Translate enterprise business requirements into robust, scalable AI agent designs with tool orchestration and routing logic.
· System Orchestration: Build integrations using Model Context Protocol (MCP) to connect AI agents with external APIs, data sources, and services.
· Workflow Automation: Engineer AI-powered agents and workflows that automate and augment knowledge work, data analysis, and software development lifecycles.
· Performance Monitoring: Implement logging, monitoring, and feedback loops to evaluate agent behavior, output quality, and guardrails.
· Cross-Functional Collaboration: Partner directly with software engineers, data scientists, and business domain experts to solve business problems and identify areas for AI integration.
Required Qualifications
· Education & Experience: Bachelor’s degree in Computer Science, Data Science, Engineering, or equivalent work experience.
· AI & Machine Learning: Strong understanding of Large Language Models (LLMs), RAG (Retrieval-Augmented Generation), Prompt Optimization, and AI Orchestration.
· Programming & Tooling: Proficiency in Python or Go. Experience with AI frameworks such as LangChain or LlamaIndex.
· Software Engineering: Solid grasp of CI/CD pipelines, Docker, Kubernetes, and Unix/Linux environments.
· Cloud Environments: Hands-on development experience in cloud infrastructures such as Microsoft Azure (preferred), Google Cloud Platform, or AWS.
· Communication: Ability to navigate corporate complexity, defend solution proposals, and articulate technical trade-offs to business stakeholders.
Preferred Qualifications
· Demonstrated hands-on application of MCP (Model Context Protocol) to integrate agents with coding and review workflows.
· Understanding of enterprise search and assistant platforms (e.g., Glean).
· Contributions to open-source AI or machine learning projects
Hope you are doing well,
Job Title: Lead Agentic AI Engineer
Location: Remote
Role Overview
As an Lead Agentic AI Engineer you will design, build, and deploy autonomous AI systems. You will empower GM’s global teams by engineering intelligent agents capable of complex reasoning, multi-agent orchestration, and autonomous task execution. Bridges advanced LLM capabilities with complex, large-scale automotive data environments.
Key Responsibilities
· Agent Architecture: Translate enterprise business requirements into robust, scalable AI agent designs with tool orchestration and routing logic.
· System Orchestration: Build integrations using Model Context Protocol (MCP) to connect AI agents with external APIs, data sources, and services.
· Workflow Automation: Engineer AI-powered agents and workflows that automate and augment knowledge work, data analysis, and software development lifecycles.
· Performance Monitoring: Implement logging, monitoring, and feedback loops to evaluate agent behavior, output quality, and guardrails.
· Cross-Functional Collaboration: Partner directly with software engineers, data scientists, and business domain experts to solve business problems and identify areas for AI integration.
Required Qualifications
· Education & Experience: Bachelor’s degree in Computer Science, Data Science, Engineering, or equivalent work experience.
· AI & Machine Learning: Strong understanding of Large Language Models (LLMs), RAG (Retrieval-Augmented Generation), Prompt Optimization, and AI Orchestration.
· Programming & Tooling: Proficiency in Python or Go. Experience with AI frameworks such as LangChain or LlamaIndex.
· Software Engineering: Solid grasp of CI/CD pipelines, Docker, Kubernetes, and Unix/Linux environments.
· Cloud Environments: Hands-on development experience in cloud infrastructures such as Microsoft Azure (preferred), Google Cloud Platform, or AWS.
· Communication: Ability to navigate corporate complexity, defend solution proposals, and articulate technical trade-offs to business stakeholders.
Preferred Qualifications
· Demonstrated hands-on application of MCP (Model Context Protocol) to integrate agents with coding and review workflows.
· Understanding of enterprise search and assistant platforms (e.g., Glean).
· Contributions to open-source AI or machine learning projects