Lead AI Platform & Automation Engineer - GCP, Vertex AI, IBM Watsonx, MLFlow, Terraform
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Job Description:
Job Summary
We are seeking an accomplished AI Application Architect to join our AI Acceleration teams and play a pivotal role in designing and delivering enterprise-scale AI and Generative AI (GenAI) systems. As an AI Architect, you will be responsible for defining application architectures for ML/GenAI solutions on cloud platforms & tools such as GCP, Vertex AI, IBM Watsonx, leading end-to-end design and implementation, and driving adoption of emerging technologies and frameworks across the organization.
This is a hands-on technical leadership role that requires deep expertise in AI/ML fundamentals, enterprise architecture design, and practical experience building production-grade AI/GenAI solutions. You will collaborate with data scientists, ML engineers, software engineers, and business stakeholders to deliver scalable, secure, and innovative AI-powered systems that create measurable business value.
Key Responsibilities
- Architect and design application and system architectures for ML/GenAI solutions on cloud platforms, tools (GCP, Vertex AI, IBM Watsonx).
- Build, deploy, and maintain enterprise-scale AI/ML applications, ensuring they are production-ready, secure, and scalable.
- Lead research and adoption of emerging AI/GenAI technologies, frameworks, and tools at the enterprise level.
- Partner with product, engineering, and business teams to translate business challenges into robust AI/GenAI solutions.
- Define end-to-end architecture for data pipelines, model development, deployment, monitoring, and retraining.
- Promote and evangelize best practices in AI/ML system design, data governance, MLOps, and ethical/responsible AI.
- Provide technical leadership and mentorship to AI/ML engineers and data scientists across business units.
- Stay current with advances in AI/ML, GenAI, LLMs, and cloud-native architecture, and identify opportunities for adoption.
- Define roadmaps and strategies that accelerate AI adoption and help the enterprise transition from descriptive to predictive and prescriptive analytics.
- Collaborate with executives and cross-functional leaders to advocate AI/GenAI capabilities and drive enterprise-wide adoption.
Required Qualifications
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related quantitative field.
Experience
- 7+ years of experience designing, building, and deploying AI/ML solutions including monitoring, scaling, and optimization in production at enterprise scale.
- Hands-on expertise in cloud platforms (GCP Vertex AI (preferred), AWS SageMaker, or Azure ML) and cloud-native application design.
- Proven experience designing enterprise-grade application architectures for ML/GenAI workloads.
- Strong programming skills in Python, Java, or C++, and proficiency with ML/AI frameworks (PyTorch, TensorFlow, Keras).
- Deep understanding of ML/AI fundamentals: statistical modeling, architectures, representation, reasoning, and generative models (LLMs, diffusion).
- Hands on experience in one or more ML domains such as NLP, computer vision, or reinforcement learning.
- Hands on experience in MLOps practices and tools (MLflow, TFX, Kubeflow, CI/CD pipelines, model monitoring).
- Strong knowledge of software architecture principles, cloud networking, and security best practices.
- Excellent communication and leadership skills, with the ability to influence stakeholders and lead technical discussions across business and engineering teams.
Preferred
- Experience leading research initiatives and driving enterprise-wide adoption of new technologies.
- Experience with cross-functional enterprise projects, including scoping, de-risking, and scaling AI/GenAI adoption.
- Knowledge of data governance, security frameworks, and regulatory compliance (GDPR, HIPAA, SOX).
- Prior experience launching AI/GenAI solutions into production at scale using both open-source and vendor platforms.
- Active involvement in the AI/ML community through publications, open-source contributions, or industry collaborations.
Employee Type:
UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.
How we score this
Lead AI Platform & Automation Engineer - GCP, Vertex AI, IBM Watsonx, MLFlow, Terraform at UPS scores 96 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.
AI Level 4. Building AI systems is the job itself: without AI, the role would not exist.
- AI Level 480 to 100
- AI Level 360 to 79
- AI Level 240 to 59
- AI Level 10 to 39
Bands come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you monitor a model once it's live, and how do you know it needs retraining?
- Walk me through a computer vision problem you solved, from raw data to a deployed model.
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- How do you think about the risk of an AI system in this kind of role failing silently?
- Walk me through how you've used Vertex AI in your day-to-day work.
Adapt your resume
- List these exact terms on your resume: Ml Ops, Computer Vision, Nlp, AI Safety, and Vertex AI. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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