Lead AI Engineer - Enterprise AI Operations
New
Remote-first (United States)Full-TimeLead
Salary204500 - 290000 USD per year
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Job Details
- Experience
- 7-10+ years
- Required Skills
- PythonTypeScriptCI/CDRESTful APIsDistributed Systems
Requirements
- 7-10+ years of professional software engineering experience designing, building, and operating complex production systems in cloud environments or equivalent practical experience.
- Proven experience building and deploying AI-powered systems in production, including agent-based or multi-step workflows (RAG, orchestration, tool-calling, memory strategies, evaluation, and failure handling).
- Strong proficiency in modern programming languages (e.g., Python, TypeScript).
- Demonstrated ability to write clean, maintainable, production-quality code.
- Deep engineering discipline across clean architecture, distributed systems, APIs, CI/CD, testing strategies, and production observability.
- Experience partnering directly with senior or executive stakeholders, translating ambiguous ideas into scalable, technically sound solutions with measurable impact across enterprise systems.
- Familiarity with AI governance, data classification, prompt injection risks, access control models, and enterprise compliance standards.
- Track record of partnering with Security on safe deployment.
Responsibilities
- Serve as the primary AI engineering partner to the CEO and executive leadership team, translating ideas into production-ready AI agents and workflows with minimal oversight.
- Independently take ideas from concept to production, shaping problem statements, designing system architecture, implementing code, validating outputs, and operationalizing solutions.
- Design and implement complex, multi-step agentic workflows, including multi-agent orchestration, retrieval-augmented generation (RAG), tool use, memory strategies, evaluation frameworks, and cross-system automation.
- Develop production-grade AI systems using modern LLMs, orchestration frameworks, and internal tooling, with strong attention to scalability, performance, observability, and clean engineering practices.
- Operationalize AI responsibly by implementing guardrails, structured evaluations, monitoring, and validation layers.
- Partner closely with Security and Legal to properly gate sensitive use cases, implementing access controls, audit logging, data minimization, and enterprise-grade governance patterns.
- Translate ambiguous, high-visibility problems into clear technical solutions, balancing speed, quality, and risk while maintaining a high bar for accuracy and trust.
- Evaluate, select, and rationalize AI tools and platforms, contributing to capability-to-tool decisions and ensuring consolidation, security alignment, and long-term sustainability.
- Support post-launch adoption and iteration, incorporating feedback, refining workflows, and continuously improving performance, usability, and measurable impact.
- Contribute to org-wide AI maturity by documenting architectural patterns, sharing best practices, and establishing repeatable approaches.
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