[Hiring] Machine Learning Engineer @NextGen Commerce

Role Description

Ford GDIA is seeking a hands-on Machine Learning Engineer to join a newly formed AI delivery team focused on building and operationalizing Agentic AI solutions that support quality engineering and enterprise problem-solving workflows. This role will work closely with business stakeholders, data scientists, and software engineers to develop production-ready AI applications using GCP technologies including Vertex AI, Gemini, BigQuery, RAG frameworks, and AI Agents.

Responsibilities

• Design, develop, and deploy production AI applications using Vertex AI, Gemini, and related GCP services.
• Build Agentic AI solutions capable of reasoning, retrieval, decision-making, and multi-step workflow execution.
• Develop and optimize Retrieval-Augmented Generation (RAG) architectures using structured and unstructured data sources.
• Create AI agents that interact with enterprise data platforms, including BigQuery.
• Build APIs and backend services that support AI-powered applications and integrations.
• Collaborate with business stakeholders to translate requirements into scalable AI solutions.
• Implement monitoring, observability, evaluation, governance, and operational support for deployed AI systems.
• Integrate AI solutions into existing enterprise systems, platforms, and workflows.
• Support AI initiatives from proof-of-concept through production deployment and ongoing optimization.

Qualifications

• 5+ years of experience in Machine Learning Engineering, AI Engineering, or related disciplines.
• Proven experience building and deploying production-grade machine learning or AI applications.
• Strong hands-on experience with Google Cloud Platform (GCP).
• Experience working with Vertex AI and enterprise-scale AI/ML solutions.
• Experience developing LLM-based and Generative AI applications.
• Strong understanding of prompt engineering techniques.
• Experience building RAG pipelines and AI-powered retrieval/search solutions.
• Strong Python development experience.
• Experience integrating with enterprise data systems and APIs.
• Strong communication skills with both technical and non-technical stakeholders.

Requirements

• BigQuery
• Gemini
• LangChain
• LangGraph
• Multi-Agent Architectures
• Vector Databases
• FastAPI, Flask, or similar backend frameworks
• CI/CD pipeline development
• Terraform and Infrastructure-as-Code practices
• GitHub Actions, Jenkins, or other DevOps tooling
• MLOps practices including model monitoring, evaluation, and governance
• Recommendation or personalization systems
• Intelligent workflow automation solutions