Exel
Director, AI and Agentic Engineering
Alameda, CA · Director
Sponsorship not specified$215k-$306kDetected 24 days ago
DatabricksAWSCloud PlatformsCI/CDPlatform EngineeringData EngineeringLLMsRAGAgentic AIAI OrchestrationComplianceRoadmappingStakeholder ManagementVendor ManagementTest AutomationLeadershipCommunicationCollaborationGxP
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odds of building a lasting career here
61Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role90
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
Lottery odds assume a STEM candidate.
Personalize to your clock →Employer immigration record
from this employer's Department of Labor filings
Green-card filing pattern in this occupation
Context, not a finding about this posting: of this employer's 3 green-card filings in this occupation, 100% were for a worker who already held the job.
Files H-1B transfers
1 transfer filing in the last year, covering 1 worker. Median labor-condition decision: 9 days. An employer that already files transfers is one that can take over an existing H-1B.
Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.
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About the role
- This role combines deep AI engineering leadership with strong product management capabilities to translate business needs into scalable, secure, measurable, and user-centered AI products.
- The base pay range for this position is $215,000 - $306,000 annually.
- The base pay range may take into account the candidate's geographic region, which will adjust the pay depending on the specific work location.
Responsibilities
- Lead the design and delivery of scalable AI platform capabilities, including model access patterns, agent orchestration, retrieval-augmented generation, evaluation frameworks, prompt and artifact management, observability, governance controls, and reusable integration patterns.
- Oversee end-to-end development of production-grade generative AI, agentic AI, and automation solutions that improve productivity, decision support, operational efficiency, and business outcomes across functions.
- Partner with business leaders, product managers, architects, data teams, cybersecurity, privacy, legal, compliance, and enterprise application teams to translate strategic business needs into secure, governed, and scalable AI solutions.
- Establish AI-native engineering standards and practices, including specification-driven development, agent-assisted software delivery, automated testing, code quality, reusable patterns, DevSecOps, CI/CD, release management, and operational support models.
- Ensure AI solutions are designed for reliability, security, scalability, auditability, explainability, human oversight, measurable autonomy, and responsible AI use consistent with company policies and regulatory expectations.
- Manage platform, and technology decisions across AI providers, cloud services, data platforms, development tools, and open-source components; assess build-versus-buy options and total cost of ownership.
- Create and monitor success metrics for AI platforms and solutions, including adoption, productivity impact, quality, latency, cost, risk reduction, reuse, user satisfaction, and business value realization.
- Lead architecture reviews, prompt and solution reviews, security reviews, model and agent evaluations, release-readiness assessments, and post-production performance monitoring.
- Develop talent, delivery practices, technical documentation, playbooks, reference architectures, and enablement materials that help teams adopt AI-native engineering safely and effectively.
- Performs other duties as assigned
Requirements
- It is not designed to contain or be interpreted as a comprehensive inventory of all duties, responsibilities and qualifications required of employees assigned to the job.
- Bachelor's degree in a related discipline and 13 years of related experience
- Master's degree in a related discipline and 11 years of related experience
- Experience with cloud AI and data platforms such as AWS, Amazon Bedrock, Databricks Mosaic AI, or equivalent technologies
- familiarity with APIs, event-driven architectures, data pipelines, identity, secrets management, and enterprise security patterns.
- Assist with operating budgets, and capital budgets if required, and control expenses to adhere to approved budgets.
- Strong ability to define platform strategy, product roadmaps, business cases, success metrics, delivery plans, and adoption approaches for enterprise AI capabilities.
Skills
- Bachelor's degree in a related discipline and 13 years of related experience; or
- Master's degree in a related discipline and 11 years of related experience; or
- Equivalent combination of education and experience.
- 5+ years in AI Engineering or Cloud platform Engineering
- Leadership experience, overseeing cross-functional teams
Compensation
- The base pay range for this position is $215,000 - $306,000 annually.
- The base pay range may take into account the candidate's geographic region, which will adjust the pay depending on the specific work location.
This listing is sourced directly from Exel's careers page and normalized into a canonical job model.