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**Senior Applied AI ML Associate - AI Innovation in Enterprise** - **Build, Deploy**, and optimize enterprise AI solutions, enhancing decision-making and automation - **Take AI from concept to production**, deploying generative AI, agents, and large language models with measurable outcomes - **Design** and govern semantic models, ontologies, and integration patterns, ensuring reliability and user experience - **Collaborate** with stakeholders, defining business needs into tangible results and mentoring talent across teams - **Establish** unified semantic layer for trusted analytics across business intelligence, machine learning, and transactional systems - **Conduct** responsible AI, implement model risk controls, and manage AI lifecycle following regulations - **PhD/Master's** in data science-related field, 8+ years industry experience in machine learning, Python, and AI deployment

Compensation: Not specified
City: Not specified
Country: United States

Full Job Description

Location: Plano, TX, United States

Build whats next in enterprise AIsolutions that materially improve how teams make decisions, automate work, and serve internal customers. You will take generative AI from concept to production, help set the standard for semantic consistency across systems, and partner closely with stakeholders to turn complex business needs into measurable outcomes. You will mentor talent and influence technical direction across Corporate Technology and supported Corporate Functions.

As an Applied AI and Machine Learning Sr. Associate n the Corporate Technology Data Science and AI team, you will Build whats next in enterprise AIsolutions that materially improve how teams make decisions, automate work, and serve internal customers. In this role, you will take generative AI from concept to production and help set the standard for semantic consistency across systems. You will partner closely with stakeholders to turn complex business needs into measurable outcomes. You will mentor talent and influence technical direction across Corporate Technology and supported Corporate Functions. If you enjoy solving hard problems with real impact, this is the opportunity.

Job Responsibilities

Build generative AI, agentic AI, and large language model solutions in Python from proof of concept through production deployment with measurable outcomes

Design context engineering approaches to improve model accuracy, latency, reliability, and end-to-end user experience

Lead enterprise semantic modeling strategy, including ontology standards, governance practices, and lifecycle management

Partner with domain experts to create scalable ontologies that represent business entities, relationships, rules, and constraints

Define semantic integration patterns across data pipelines, application programming interfaces (APIs), data contracts, and experience layers to resolve semantic conflicts

Establish and govern a unified semantic layer that enables trusted analytics across business intelligence, machine learning, and transactional systems

Enable intelligent workflows and AI agents using ontology-driven context, semantic reasoning, and orchestration approaches

Build and maintain pipelines and frameworks for model training, evaluation, optimization, monitoring, and machine learning operations

Implement responsible AI practices, model risk controls, and governance aligned to regulated environments

Mentor engineers and data scientists, raising the bar on engineering rigor, reuse, and continuous improvement across the team

Required Qualifications, Capabilities, and Skills

Masters degree in a data science-related discipline and eight years of industry experience, or PhD in a data science-related discipline

Demonstrated experience developing and deploying machine learning and generative AI solutions using Python

Proven ability to write and maintain production-quality code, including documentation and maintainable design patterns

Experience building automated testing practices, including unit tests, and implementing continuous integration pipelines

Experience building and managing data pipelines and processing workflows for analytics and machine learning use cases

Strong scientific thinking and structured problem-solving skills, including hypothesis-driven analysis and metric definition

Strong written and verbal communication skills, with the ability to explain complex concepts to technical and non-technical stakeholders

Demonstrated ownership and attention to detail when operating in ambiguous, complex problem spaces

Ability to work independently while collaborating effectively across product, engineering, data, and business partners

Preferred Qualifications, Capabilities, and Skills

Experience designing or governing semantic models and ontologies, including taxonomy design and lifecycle governance

Experience implementing retrieval-augmented generation, tool use, and evaluation strategies for large language model applications

Familiarity with responsible AI techniques, including bias testing, explainability approaches, and model monitoring standards

Experience designing scalable architectures for real-time or near-real-time inference and intelligent workflow orchestration

Experience influencing cross-functional technical direction and mentoring engineers through design reviews and delivery execution

#LI-RB3

Lead applied AI and generative AI solutions from concept to production, setting enterprise semantic standards.

Full Job Description