Director of AI at Jobtailor — NeverHard

Lead technical strategy, architecture, and delivery of AI applications from discovery through production and scale Work with client executives, product leaders, architects, and engineering teams to identify AI opportunities and create technical roadmaps Design AI applications combining models, enterprise data, APIs, software components, user experiences, and human workflows Guide agentic workflows, RAG, enterprise search, predictive models, and AI capabilities embedded in digital products Decide when to use deterministic software, machine learning, LLMs, human review, or combinations Establish evaluation-driven development with test datasets, error analysis, deterministic checks, model-based evaluation, and business outcome measurement Ensure production reliability, observability, scalability, latency, maintainability, security, and cost requirements Guide CI/CD, model and prompt versioning, monitoring, tracing, regression testing, and optimization Provide hands‑on technical leadership through prototyping, architecture reviews, code reviews, troubleshooting, and delivery oversight Use AI-assisted development and coding agents with 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 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 Experience designing, building, and operating AI or machine learning applications in production Strong foundation in system design, APIs, data architecture, testing, security, cloud infrastructure, and production operations Hands‑on proficiency in Python and modern application, data, and AI engineering frameworks 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 probabilistic-output systems 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 Success in consulting, professional‑services, or complex client‑facing environments Ability to work across executive conversations, product decisions, architecture discussions, and detailed technical problem‑solving Excellent communication skills with technical and non‑technical audiences Degree in computer science, software engineering, artificial intelligence, data science, or related field—or equivalent professional experience Preferred: experience in regulated, privacy‑sensitive, or large‑scale enterprise environments; model fine‑tuning; open‑source models; multimodal AI; voice agents; computer‑use agents; enterprise AI security controls; internal AI platforms; organizational AI strategy; certifications or delivery experience with GCP, Snowflake, Databricks, or comparable platforms Core Competencies Demonstrates expertise in leading the technical strategy and architecture of AI applications, with a strong foundation in system design, data architecture, and production operations. Proficient in Python and modern AI engineering frameworks, with a focus on ensuring production reliability, scalability, and security. Highest-signal resume keywords AI Application Development Machine Learning Solutions Leadership Cloud Infrastructure Deployment (GCP, AWS, Azure) System Design and Data Architecture CI/CD and MLOps Practices ATS Optimization Keywords Hard Skills Python AI Engineering Frameworks Machine Learning Data Architecture System Design Model Evaluation Error Analysis Production Operations API Development Cloud‑Native Architecture Soft Skills Excellent Communication Skills Client‑Facing Experience Technical Mentorship Industry Keywords AI Applications Machine Learning Enterprise Data Regulated Environments Privacy‑Sensitive Environments Organizational AI Strategy Tools & Technologies GCP AWS Azure Containers CI/CD Tools MLOps LLMOps #J-18808-Ljbffr