Senior Frontier AI Deployment Engineer

About the Role

We are seeking a Senior Frontier AI Deployment Engineer to bridge product, engineering, and business stakeholders as we design and deliver production-oriented generative AI solutions. This role combines the practical judgment of an AI engineer with the ownership mindset of a product lead. The ideal candidate is an exceptional communicator who can clarify requirements, shape solution approaches, guide distributed teams, and keep delivery moving from discovery through launch. This is not a pure software-development role. Strong technical fluency and hands-on delivery experience are required, but success depends more on systems thinking, stakeholder alignment, clear written and verbal communication, and sound product judgment than on writing large volumes of code.

Responsibilities

• Lead discovery with business, product, data, security, and engineering stakeholders; convert ambiguous needs into clear use cases, requirements, acceptance criteria, risks, and delivery plans.
• Act as the connective layer among teams in India, Europe, and North American Central and Pacific time zones, creating crisp decisions, handoffs, documentation, and follow-through.
• Shape solution designs for retrieval-augmented generation (RAG), AI agents, tool use, orchestration, evaluation, guardrails, observability, and human-in-the-loop workflows.
• Guide prototypes and production implementations, making pragmatic tradeoffs across user value, model quality, latency, cost, security, reliability, and maintainability.
• Partner with engineers and architects on interfaces, data flows, integrations, deployment patterns, and operational readiness; contribute code or technical artifacts when it accelerates delivery.
• Own stakeholder-facing demos and the supporting demo sites and environments, including setup, access, content and data readiness, reliability, presentation quality, and ongoing maintenance; lead stakeholder updates and technical workshops.
• Define meaningful success measures and evaluation approaches for AI quality, safety, adoption, and business impact.
• Stay current on rapidly changing AI capabilities and translate new developments into practical recommendations rather than technology for its own sake.

Requirements

• 6+ years of experience in software, data, ML/AI, solutions engineering, technical product delivery, or a closely related field.
• Demonstrated senior-level ownership of ambiguous, cross-functional initiatives, including driving decisions and delivery across multiple phases rather than contributing only to isolated technical tasks.
• Demonstrated delivery experience with at least one generative AI system, such as RAG, AI agents, copilots, semantic search, or LLM-enabled workflow automation. Candidates need not have built every component alone, but must clearly explain their contribution and the system’s end-to-end behavior.
• Excellent written, verbal, and visual communication, including the ability to explain technical choices to nontechnical stakeholders and business context to engineers.
• Experience eliciting requirements, resolving ambiguity, prioritizing scope, defining acceptance criteria, and driving cross-functional execution.
• Working knowledge of modern generative AI patterns: prompt and context design, embeddings and retrieval, agent/tool orchestration, model selection, evaluation, safety controls, observability, and production operations.
• Ability to review architecture and code, troubleshoot across system boundaries, and produce lightweight prototypes or examples. Deep specialization in application coding is not required.
• Experience collaborating across countries and time zones, with disciplined asynchronous documentation and handoffs.
• Practical understanding of enterprise concerns including data privacy, security, access control, responsible AI, compliance, cost, and reliability.

Preferred Qualifications

• Hands-on experience with AWS generative AI services, especially Amazon Bedrock and Amazon Bedrock AgentCore.
• Experience in a forward-deployed, solutions architecture, technical program leadership, consulting, sales engineering, or product ownership role.
• Experience establishing AI evaluation sets, quality metrics, red-team practices, guardrails, monitoring, or production feedback loops.
• Familiarity with cloud-native architectures, APIs, event-driven systems, vector databases, identity and access management, and CI/CD.
• Experience facilitating executive or customer-facing workshops and turning outcomes into an executable product or engineering backlog.

Schedule

Preferred location: Eastern (or) Central U.S. time zones, or a location with meaningful overlap across those hours. The role requires planned overlap with colleagues in India and Europe. Flexibility for occasional early or late meetings is expected; sustainable schedules and strong asynchronous practices are equally important.

What Success Looks Like

• Stakeholders understand what is being built, why it matters, and how success will be measured.
• Distributed teams receive timely decisions, complete context, and low-friction handoffs.
• AI concepts move from discovery to production with clear quality, safety, cost, and operational criteria.
• The role earns trust across product, engineering, and business groups by communicating candidly and delivering predictably.