Senior AI Platforms Engineer

United States·WashingtonRemoteFull Timesenior

OtherEngineer

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Overview

hatch I.T. is partnering with Expression to find a Senior AI Platforms Engineer.

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OtherEngineer

hatch I.T. is partnering with Expression to find a Senior AI Platforms Engineer. See details below:

About the Role

~1 min read

Expression is seeking an experienced Senior AI Platform Engineer to join their Product Team and contribute to the evolution of their Agentic AI platform. We are building an enterprise-grade platform for their National Security and Federal Civilian customers that moves Generative AI from "chatting" to "doing.” This platform is the intelligent backbone for their semi-autonomous agents and the AI governance framework that manages them.

The ideal candidate thrives in a highly technical, collaborative environment and has a strong bias for action. You are comfortable taking ownership, elevating your team through clear communication and solid technical judgment, and continually improving how we build their platform.

Founded in 1997 and headquartered in Washington DC, Expression provides data fusion, data analytics, software engineering, information technology, and electromagnetic spectrum management solutions to the U.S. Department of Defense, Department of State, and national security community. Expression’s “Perpetual Innovation” culture focuses on creating immediate and sustainable value for their clients via agile delivery of tailored solutions built through constant engagement with their clients. Expression was ranked #1 on the Washington Technology 2018's Fast 50 list of fastest growing small business Government contractors and a Top 20 Big Data Solutions Provider by CIO Review.

Contribute to the design and evolution of Expression's Agentic AI platform by defining scalable architectures for enterprise AI applications and services, including system architecture, core platform components, tooling, automation, coding standards, and platform performance, scalability, and reliability.
Architect, deliver, and optimize production-grade LLM services, agent workflows, orchestration layers, retrieval pipelines, and vector search technologies.
Provide technical leadership and mentorship: guide architecture and design reviews, grow engineers, and raise the bar for engineering quality and technical decision-making across the team.
Develop high-performance Python APIs and scalable backend services using FastAPI, asynchronous programming techniques, and modern software engineering practices. Set the technical direction and engineering standards for high-performance, scalable backend services and APIs, and champion modern software engineering best practices across the team.
Build and maintain the underlying AI platform that supports secure, observable, resilient, and production-ready AI workloads.
Design and implement AI governance capabilities, including guardrails, policy enforcement, model lifecycle management, responsible AI practices, and compliance with federal security and governance requirements.
Develop automated testing and evaluation strategies for AI-enabled applications, including prompt evaluation, model evaluation, regression testing, integration testing, and quality assurance.
Implement LLM observability, monitoring, logging, telemetry, performance metrics, and resilience strategies to ensure reliable production operation.
Design, implement, and maintain CI/CD pipelines and deployment automationDefine and drive CI/CD and deployment automation strategy to support secure, efficient delivery of AI services.
Collaborate with Product, UX, Infrastructure, Security, and other cross-functional teams to continuously improve platform capabilities and deliver customer-focused solutions.
Collaborate with clients, lead product demonstrations, and communicate technical concepts and platform capabilities to both technical and executive audiences.
Produce clear technical documentation, architecture diagrams, design proposals, and other engineering artifacts.

Bachelor 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 designing and building enterprise software platforms.
Demonstrated technical leadership: leading architecture and design for complex production platforms, mentoring engineers, and driving technical decisions across teams.
Hands-on experience designing and building agent orchestration and LLM workflows (custom or framework-based); hands-on experience with frameworks such as LangGraph, LangChain, CrewAI, or Strands is useful but not required.
Expert-level Python, including asyncio and asynchronous programming, FastAPI, Pydantic, type hinting, and modern development practices.
Strong proficiency in TypeScript and Node.js for building production platform services, APIs, and SDKs.
Proven experience implementing retrieval architectures.
Experience designing and operating vector databases and both lexical and semantic search systems.
Strong experience 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.
Experience with Kubernetes, Infrastructure as Code (Terraform and Helm, or equivalent), and modern cloud-native deployment practices.
Familiarity with AI governance, model lifecycle management, prompt engineering, and responsible AI principles.
Experience implementing LLM observability, monitoring, evaluation frameworks, and production telemetry.
Strong written and verbal communication skills with the ability to produce technical documentation and communicate effectively with technical and executive stakeholders.

Experience building enterprise-scale Agentic AI platforms.
Experience implementing automated prompt evaluation, model evaluation, and AI quality assurance frameworks.
Experience developing AI guardrails, policy engines, and secure AI deployment patterns.
Experience with PostgreSQL performance tuning, asynchronous database drivers (asyncpg), indexing, and query optimization.
Experience building event-driven or distributed systems using microservices architectures.
Experience supporting National Security or Federal Civilian customers.