Director of AI

Lead the technical strategy architecture and delivery of AI applications from discovery and experimentation through production and scale.

Work with client executives product leaders architects and engineering teams to identify valuable AI opportunities and translate them into achievable technical roadmaps.

Design AI applications that combine models enterprise data APIs software components user experiences and human workflows.

Guide the implementation of agentic workflows retrieval-augmented generation enterprise search predictive models and AI capabilities embedded in digital products.

Determine when to use deterministic software machine learning large language models human review or a combination of approaches.

Establish evaluation-driven development practices including test datasets error analysis deterministic checks model-based evaluation and business outcome measurement.

Ensure solutions meet production requirements for reliability observability scalability latency maintainability security and cost.

Guide practices for CI/CD model and prompt versioning monitoring tracing regression testing and ongoing optimization.

Provide hands-on technical leadership through prototyping architecture reviews code reviews troubleshooting and delivery oversight.

Use AI-assisted development and coding agents effectively while maintaining appropriate verification security and human oversight.

Support proposals discovery workshops solution design estimates and executive presentations.

Develop reusable AI engineering patterns reference architectures accelerators and delivery standards.

Contribute to hiring technical mentorship and the continued growth of APPLYs AI capability.

10 years of experience across software engineering data engineering machine learning or related technology disciplines including significant experience leading AI or ML solutions.

Demonstrated experience designing building and operating AI or machine learning applications in productionnot only proofs of concept.

Strong software engineering foundation including system design APIs data architecture testing security cloud infrastructure and production operations.

Hands-on proficiency in Python and experience with modern application data and AI engineering frameworks.

Strong understanding of LLMs RAG context engineering agentic workflows tool use structured outputs and model evaluation.

Experience grounding AI systems in enterprise data including structured data documents semantic models vector stores or knowledge graphs.

Experience establishing evaluation and error-analysis practices for systems with probabilistic outputs.

Ability to balance model quality reliability latency cost security and user experience.

Experience with cloud-native architecture and production deployment on GCP AWS or Azure.

Familiarity with containers CI/CD observability and MLOps or LLMOps practices.

Demonstrated success in a consulting professional-services or complex client-facing environment.

Ability to move comfortably between executive conversations product decisions architecture discussions and detailed technical problem-solving.

Excellent communication skills with both technical and non-technical audiences.

A degree in computer science software engineering artificial intelligence data science or a related fieldor equivalent professional experience.

Experience delivering AI solutions in regulated privacy-sensitive or large-scale enterprise environments.

Experience with model fine-tuning open-source models multimodal AI voice agents or computer-use agents.

Experience implementing enterprise AI security controls including prompt-injection defenses tool permissions and human approval gates.

Experience building internal AI platforms reusable agent frameworks or developer enablement capabilities.

Experience contributing to organizational AI strategy operating models or technical standards.

Professional certifications or significant delivery experience with GCP Snowflake Databricks or comparable platforms.