Technical Director, AI Enterprise Architect
New
L
Locus RoboticsRobotics, AI
United StatesFull-TimeDirector
Salary$200,000 - $300,000 annually, plus bonus and equity
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Job Details
- Languages
- English
- Experience
- 5+ years
- Required Skills
- AWSPythonGCPMachine LearningAzureData engineeringPrompt EngineeringLLM
Requirements
- Bachelor's degree in Computer Science, Engineering, or a related technical field (Master's or PhD preferred).
- 5+ years leading enterprise AI or digital transformation initiatives with demonstrated ownership of strategy through execution.
- 5+ years of hands-on experience in software engineering, data engineering, or AI/ML roles.
- Proficiency in Python and modern cloud platforms (AWS, Azure, or GCP).
- Proven experience designing and deploying production-grade AI systems and agentic workflows.
- Deep expertise in LLM-based systems including RAG, prompt engineering, and tool integration.
- Strong foundation in data engineering, ETL/ELT pipelines, APIs, and Lakehouse environments (e.g., Databricks).
- Working knowledge of classical machine learning techniques including regression, classification, and anomaly detection.
- Demonstrated ability to translate business problems into scalable technical solutions with strong business acumen.
- Excellent English communication and leadership skills with the ability to engage executive stakeholders.
Responsibilities
- Define and drive the company-wide AI roadmap, including prioritization frameworks, sequencing of initiatives, and executive alignment.
- Partner with leaders across Sales, Customer Success, Finance, Operations, and Marketing to redesign processes into AI-native, automated workflows.
- Design, build, and deploy production-grade AI systems, including agentic workflows that automate end-to-end processes.
- Architect scalable LLM-powered systems, including retrieval-augmented generation (RAG), unified context layers, and integration frameworks.
- Design and implement robust data pipelines, integration layers, and shared infrastructure for enterprise-wide AI capabilities.
- Establish frameworks for model governance, risk management, data access, and security.
- Drive AI adoption across the organization by mentoring leaders and establishing best practices.
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