Forward Deployed AI Engineer

Role overview A Forward Deployed AI Engineer embeds an AI governance and deployment platform inside large, heavily regulated organizations such as financial institutions, hospitals, insurers, and public agencies. The role blends hands-on software engineering with customer-facing advisory work, owning each deployment from first integration through production rollout and turning real-world field signals back into product improvements.

Responsibilities - Own end-to-end customer deployments, from initial integration design to a production system the customer trusts operationally. - Integrate the platform into complex enterprise environments, including identity providers, data platforms, model and agent stacks, and existing security tooling. - Translate customer governance and compliance requirements into concrete, auditable policies and prove they satisfy external auditors. - Debug hard production problems across the stack, often inside partially opaque customer environments on tight timelines. - Build integrations, connectors, SDKs, and internal tooling that make each subsequent deployment faster and more repeatable. - Represent engineering in architecture reviews, security assessments, and go/no-go decisions with senior customer stakeholders. - Carry deployment learnings back to product and engineering so field gaps shape the platform roadmap. - Codify deployment patterns through playbooks, reference architectures, and reusable practices.

Requirements - Bachelor's, Master's, or PhD in Computer Science, Engineering, or a related field, or equivalent experience shipping production software. - 6+ years of software engineering, including time in customer-facing or deployment-heavy roles such as forward-deployed, solutions, or platform/integration engineering. - Strong fundamentals in Python plus at least one of Go, TypeScript, or Java, with the ability to ramp quickly in unfamiliar codebases. - Hands-on experience integrating software into enterprise environments using REST and gRPC APIs, OAuth/OIDC and SSO, containers and Kubernetes, and a major cloud (AWS, GCP, or Azure). - Working familiarity with modern AI systems including LLMs, agents, RAG, and related tooling (for example, OpenAI and Anthropic APIs, LangChain, and vector databases). - Comfort leading technical conversations with senior stakeholders and staying calm during high-stakes cutovers. - Willingness to travel up to roughly 30%, including to regulated and occasionally air-gapped sites.

Nice to have - Experience building reusable deployment tooling, SDKs, or reference architectures adopted across multiple engagements. - Background working directly with auditors, risk officers, or security review boards in regulated industries.