Senior AI Engineer

GXO Logistics•Greenwich, CT

About The Position

Are you ready to help shape the future of enterprise AI at a rapidly growing global company? As an AI Solutions Engineer, you will be responsible for designing, building, and implementing AI-powered solutions that automate and enhance business processes across HR, Finance, Sales, and other enterprise functions. You'll leverage large language models (LLMs), orchestration frameworks, and Google Cloud Platform (GCP) technologies to deliver scalable, secure, and production-ready AI applications. If you're excited about solving complex business challenges with cutting-edge AI, join us at GXO.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience
Experience designing, building, configuring, or integrating enterprise technology solutions in complex environments
Experience working with large language models (LLMs) or generative AI technologies, including prompt engineering and AI workflow development
Experience building AI-driven workflows or agentic systems using orchestration frameworks such as ADK, LangChain, or similar technologies
Strong programming experience with Python for developing, integrating, testing, and deploying enterprise solutions
Hands-on experience with Google Cloud Platform (GCP), including services such as Vertex AI, Cloud Run, BigQuery, and related cloud-native technologies
Experience integrating APIs and enterprise platforms such as HRIS, CRM, ERP, finance, or document management systems
Understanding of software development lifecycle (SDLC), Agile methodologies, and cross-functional delivery practices
Experience implementing secure, scalable, and production-ready AI applications
Strong written and verbal communication skills with the ability to collaborate across technical and business teams
Ability to work independently while managing multiple priorities in a fast-paced environment

Nice To Haves

Experience with Retrieval-Augmented Generation (RAG) architectures
Experience handling unstructured data and multi-step AI workflow orchestration
Cloud, AI, software engineering, or solution architecture certifications
Experience implementing Responsible AI, governance, or AI compliance frameworks
Experience working with enterprise architecture and security standards
Familiarity with containerized deployments and enterprise integration patterns
Strong analytical and problem-solving skills with the ability to translate business requirements into technical solutions
Ability to identify opportunities for automation, process optimization, and AI innovation
Excellent organizational and project management skills with the ability to manage multiple initiatives simultaneously
Strong collaboration and interpersonal skills with a customer-focused mindset
Ability to provide technical guidance, mentorship, and knowledge sharing across engineering teams
High attention to detail with a commitment to delivering secure, reliable, and scalable AI solutions
Self-motivated with a strong sense of ownership and accountability

Responsibilities

Design, build, configure, and enhance AI-powered applications, workflows, and automations using LLMs, orchestration frameworks (such as ADK or LangChain), and Google Cloud Platform services
Translate business processes into scalable AI solutions by integrating large language models, APIs, and enterprise systems
Develop, test, troubleshoot, and deploy AI solutions through prototype, testing, and production readiness
Integrate AI applications with enterprise platforms such as HRIS, CRM, ERP, finance systems, and document management solutions
Implement human-in-the-loop workflows, validation checkpoints, and escalation paths to ensure responsible and auditable AI usage
Collaborate with Product, Deployment, PMO, Architecture, Security, Data, and AI Platform teams to deliver enterprise AI initiatives
Identify and mitigate AI-related risks, including hallucinations, data sensitivity, bias, and inappropriate automation
Create reusable implementation patterns, documentation, and engineering standards to improve delivery consistency
Support testing, deployment, integration, and continuous improvement of AI solutions across the enterprise
Ensure solutions align with enterprise architecture, security, governance, and Responsible AI standards