Director of AI

RESPONSIBILITIES

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 APPLY's AI capability.

REQUIREMENTS

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 production - not 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 field - or equivalent professional experience.

NICE TO HAVES

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.

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