Senior Artificial Intelligence Engineer

Globality; Palo Alto (CA); On-site; USD 140,000 - 200,000; Full time

Benefits offered by this job

Premium Health Benefits

Distributed Team Building

Learning & Development

Award Winning Culture

Job summary

Globality is seeking an AI Platform Engineer to design and implement scalable AI platforms using Python and MLOps tooling. You will interface with large language models through prompting frameworks, LangGraph, and orchestration services, enabling teams to deploy and iterate on intelligent applications.

The role emphasizes data pipelines, model monitoring, and rigorous testing, with a focus on secure, ethical AI usage and cross-team collaboration across engineering and product groups.

Qualifications

• 4+ years of experience building production-grade AI/ML platforms.
• Experience with large-scale data pipelines (e.g., Apache Spark, Kafka).
• Certifications in cloud platforms, ML engineering, or specific AI tooling.
• Strong software engineering skills in Python and familiarity with Java/Go and OOP/data structures.
• Awareness of AI ethics, biases, fairness, and data privacy protocols.
• Excellent communication and collaboration across engineering and product teams.

Responsibilities

• Design and implement scalable AI platforms using Python and MLOps tooling.
• Interface with LLMs via prompting frameworks, agentic workflows, and orchestration services.

Skills

MLOps

Python

Cloud deployment

Data pipelines

Education

Bachelor’s or Master’s in Computer Science, Engineering, or related field

Tools

Docker

Kubernetes

Apache Spark

Kafka

LangGraph

Job description

• This role is ideal for someone passionate about enabling AI innovation through robust infrastructure, intuitive tooling, and seamless integration of cutting-edge models.
• You’ll be at the forefront of operationalizing AI—designing systems that empower teams to build, deploy, and iterate on intelligent applications with speed and reliability
• Platform Architecture & Development: Design and implement scalable AI platforms using Python. Integrate MLOps tools for model versioning, deployment, monitoring, and lifecycle management
• AI Tooling & LLM Capabilities: Build tools and abstractions to interface with large language models, including prompting frameworks, agentic workflows (e.g., with LangGraph), and LLM orchestration services
• Data Engineering & ETL: Collaborate with data teams to build robust ETL pipelines, preprocess training data, and construct feature workflows that feed AI models at scale
• Reliability & Monitoring: Implement model monitoring dashboards to ensure platform reliability and performance, and investigate production prompt results
• Research & Innovation: Stay ahead of cutting-edge trends in Generative AI. Prototype AI scenarios that unlock new product or operational value
• Testing, Documentation & Standards: Define rigorous unit, integration, and performance testing methodologies. Maintain comprehensive documentation and enforce best practices for ethical and secure AI usage

Benefits

• Premium Health Benefits
• Distributed Team Building
• Learning & Development
• Award Winning Culture

Solid understanding of MLOps principles: CI/CD, model versioning, monitoring, metricsFamiliarity with LLM prompting design, agentic workflows (e.g. LangGraph)Nice to HaveExceptional communication and collaboration across engineering and product teamsBachelor’s or Master’s in Computer Science, Engineering, or related fieldAwareness of AI ethics, biases, fairness, and data privacy protocols4+ years of experience building production-grade AI/ML platforms or developer-centric AI toolsExperience with large-scale data pipelines (e.g., Apache Spark, Kafka)Certifications in cloud platforms, ML engineering, or specific AI toolingStrong software engineering skills in Python (preferred) and familiarity with Java/Go and OOP/data structuresExperience building and deploying on cloud platforms (AWS, Google Cloud, Azure); familiar with Docker and Kubernetes