Forward Deployed AI Engineer
Spotted 2h agofulltime
Job description
About this role
Employer-provided description, formatted for easier reading.
Forward Deployed AI Engineer
Location: San Francisco, CA
Compensation: $180-200K + Bonus
US Citizens only; No Visa Sponsorship
Candidates must be local
Responsibilities:
- Partner with stakeholders to identify operational friction, map complex systems, and translate business challenges into clear, measurable AI use cases.
- Define both long-term technical visions and immediate, high-impact MVPs to validate value quickly.
- Design, code, and deploy Generative AI solutions, AI agents, and automated workflows integrated with enterprise APIs, databases, and applications.
- Implement robust human-in-the-loop mechanisms, fallback controls, and enterprise-grade security and governance.
- Collaborate directly with software teams to scale prototypes into reliable, production-ready systems.
- Leverage coding agents and modern AI-driven development workflows (specification-driven development, context management) to accelerate delivery.
- Convert custom deployment patterns into reusable assets, reference architectures, and paved-road tools to inform the broader enterprise AI roadmap.
- Establish frameworks to measure model performance, UX, latency, cost, and business impact.
- Implement testing, monitoring, and controls to mitigate failure modes and comply with security, privacy, and responsible AI policies.
- Communicate complex technical concepts to non-technical stakeholders and executive leadership.
- Influence cross-functional decisions without direct authority and mentor partner teams through digital transformation.
Qualifications
- 5+ years in software engineering, solution architecture, or technical consulting with a track record of shipping end-to-end products.
- Fluency in software engineering (backend, web, mobile, distributed systems, or API/workflow orchestration) with practical experience using coding agents in structured workflows.
- Demonstrated capability to analyze end-to-end systems, articulate technical tradeoffs, and influence cross-functional decisions without direct authority.
- Bachelor's degree in Computer Science, Engineering, or a related field (or equivalent practical experience).
Preferred
- Experience building with LLMs, RAG, vector databases, tool calling, Model Context Protocols, or frameworks like LangGraph, LangChain, Temporal, or LlamaIndex.
- Hands-on experience with AI evaluation metrics, observability tools, and responsible AI safeguards (human-in-the-loop).
- Track record of driving technology adoption within financial services or other highly regulated industries.
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