Director of AI

Key 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

• At least 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.