Senior AI Solutions Engineer – Customer Success and Deployment

Oracle Oracle · Enterprise · Reston, VA +1

This role is for a Senior AI Solutions Engineer focused on customer success and deployment of LLMs on Oracle Cloud Infrastructure (OCI), particularly in government and sovereign environments. The engineer will act as a technical interface, guiding customers through deployment, optimization, and operational phases, ensuring AI solutions meet mission requirements and performance expectations. Responsibilities include solution architecture, performance analysis, RAG implementation, and cross-functional collaboration.

What you'd actually do

  1. Serve as the primary technical representative for Oracle during customer AI and Generative AI deployments.
  2. Support the deployment, validation, and optimization of Large Language Models (LLMs) running on Oracle GenAI Services and OCI infrastructure, including isolated and sovereign cloud environments.
  3. Analyze and improve solution performance across throughput, latency, Time to First Token (TTFT), scalability, context utilization, resource efficiency, and overall user experience.
  4. Understand customer evaluation methodologies, benchmark frameworks, and acceptance criteria.
  5. Partner with Oracle engineering, product management, cloud operations, networking, security, and support teams to deliver successful customer outcomes.

Skills

Required

  • Strong understanding of Large Language Models (LLMs), Generative AI systems, inference architectures, and production AI application deployment.
  • Experience with prompt engineering, Retrieval-Augmented Generation (RAG), embedding models, vector databases, model evaluation methodologies, and model adaptation techniques.
  • Ability to explain AI model capabilities, limitations, risks, and expected behaviors to technical and non-technical stakeholders.
  • Experience with enterprise AI platforms such as Oracle GenAI Service, Azure OpenAI Service, Amazon Bedrock, Google Vertex AI, or similar technologies.
  • Strong understanding of cloud infrastructure, networking, security, distributed systems, and cloud-native architectures.
  • Familiarity with Kubernetes, containerized applications, and supporting production workloads in regulated, sovereign, government, or isolated cloud environments.
  • Experience presenting technical solutions to customer executives, architects, and engineering teams
  • Experience integrating LLM services and APIs into enterprise applications and business workflows.
  • Familiarity with AI development frameworks and tooling such as LangChain, LlamaIndex, LiteLLM, OpenAI-compatible APIs, and agent frameworks.
  • Understanding of API management, authentication and authorization, token management, rate limiting, observability, and monitoring practices.
  • Experience analyzing and optimizing AI workload performance, including throughput, latency, concurrency, capacity planning, token consumption, and request lifecycle behavior.
  • Ability to diagnose and resolve issues across application, model, networking, infrastructure, and operational layers.
  • Experience using monitoring, observability, and operational analytics tools to support performance improvement, root cause analysis, and production operations.
  • Strong analytical, problem-solving, and cross-functional collaboration skills in complex technical environments.

Nice to have

  • Experience with OCI and Oracle Cloud technologies.
  • Experience supporting AI workloads in OCI Dedicated Region, OCI Isolated Region, government cloud, or sovereign cloud environments.
  • Knowledge of GPU infrastructure and AI inference.

What the JD emphasized

  • MUST possess or have the ability to obtain and maintain an active TS/SCI with poly
  • Full time in office position.

Other signals

  • customer-facing technical leadership
  • deployment and optimization of LLMs on cloud infrastructure
  • performance analysis and tuning for AI workloads
  • collaboration with engineering and product teams