Position overviewWe 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.
What you will do- 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.
Required qualifications- 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.
Working modelPreferred 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.
