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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior Frontier AI Deployment Engineer - **Company:** CENTRIC SOFTWARE INC - **Location:** Austin, TX, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Engineering, Continuous Integration, Identity and Access Management, Microsoft Copilot, Search Technologies, Software Deployment, Data Streaming, Systems Integration, Working Model 2D, Retrieval-Augmented Generation, Large Language Models, AgentCore, Agentic-AI, Event Driven Architecture, Amazon Bedrock, Low Latency - **Published:** November 2, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-frontier-ai-deployment-engineer-austin-tx--c41ebf0e-6359-49ed-853f-b2904cb897e5 ## About the Role Artificial Intelligence (AI), Retail, Process Development, Artificial Intelligence (AI), Sales, Consumer Branding, Civil Engineering, Bridge Building, Product Engineering, Presentation/Verbal Skills, 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., * 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., * 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 model 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. ## Description * 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., * 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.