Senior AI Platforms Engineer

This is a fully remote position, open to applicants in Washington.

📋 Description

• Participate in the design and advancement of Expression's Agentic AI platform by establishing scalable architectures for enterprise AI applications and services, which include system architecture, core platform components, tooling, automation, coding standards, and platform performance, scalability, and reliability.

• Design, implement, and enhance production-grade LLM services, agent workflows, orchestration layers, retrieval pipelines, and vector search technologies.

• Offer technical guidance and mentorship: oversee architecture and design reviews, nurture engineers, and elevate the standards for engineering quality and technical decision-making within the team.

• Create high-performance Python APIs and scalable backend services utilizing FastAPI, asynchronous programming techniques, and contemporary software engineering practices. Establish the technical direction and engineering standards for high-performance, scalable backend services and APIs while advocating for modern software engineering best practices throughout the team.

• Construct and sustain the foundational AI platform that enables secure, observable, resilient, and production-ready AI workloads.

• Design and implement AI governance features, such as guardrails, policy enforcement, model lifecycle management, responsible AI practices, and adherence to federal security and governance standards.

• Formulate automated testing and evaluation strategies for AI-enabled applications, encompassing prompt evaluation, model evaluation, regression testing, integration testing, and quality assurance.

• Deploy LLM observability, monitoring, logging, telemetry, performance metrics, and resilience strategies to guarantee dependable production operation.

• Design, implement, and sustain CI/CD pipelines and deployment automation, while defining and advancing the CI/CD and deployment automation strategy to facilitate secure and efficient delivery of AI services.

• Work alongside Product, UX, Infrastructure, Security, and other cross-functional teams to continuously refine platform capabilities and deliver solutions focused on customer needs.

• Engage with clients, lead product demonstrations, and effectively communicate technical concepts and platform capabilities to both technical and executive audiences.

• Create clear technical documentation, architecture diagrams, design proposals, and various engineering artifacts.

⛳️ Requirements

• Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Data Science, or related fields; an advanced degree is preferred.

• 8–10+ years of professional experience in Software Engineering, specifically in designing and constructing enterprise software platforms.

• Proven technical leadership: directing architecture and design for complex production platforms, mentoring engineers, and influencing technical decisions across teams.

• Practical experience in designing and building agent orchestration and LLM workflows (whether custom or framework-based); familiarity with frameworks such as LangGraph, LangChain, CrewAI, or Strands is beneficial but not mandatory.

• Expert-level proficiency in Python, including asyncio and asynchronous programming, FastAPI, Pydantic, type hinting, and modern development practices.

• Strong capabilities in TypeScript and Node.js for developing production platform services, APIs, and SDKs.

• Established experience in implementing retrieval architectures.

• Experience in designing and maintaining vector databases and both lexical and semantic search systems.

• Significant experience in designing distributed architectures for LLM-based applications.

• Experience with self-hosted and edge model deployment: serving open and small language models (SLMs) with vLLM, Ollama, or llama.cpp, routing through LiteLLM, and autoscaling inference on Kubernetes (e.g., Karpenter) for on-premises, disconnected, and tactical environments.

• Experience deploying applications using Docker and cloud-native services within AWS or Azure.

• Experience implementing CI/CD pipelines using GitLab CI, GitHub Actions, or similar automation platforms.

• Familiarity with Kubernetes, Infrastructure as Code (Terraform and Helm, or equivalent), and modern cloud-native deployment practices.

• Understanding of AI governance, model lifecycle management, prompt engineering, and responsible AI principles.

• Experience implementing LLM observability, monitoring, evaluation frameworks, and production telemetry.

• Excellent written and verbal communication skills, capable of producing technical documentation and effectively engaging with technical and executive stakeholders.

🏝️ Benefits

• 401k matching

• PPO and HDHP medical/dental/vision insurance

• Education reimbursement up to $10,000/yr

• Complimentary life insurance

• Generous PTO and 11 days of holiday leave

• Onsite gym facility and trainer

• Commuter Benefits Plan

• In-office Cold Brew Coffee