Senior Platform Engineer (AI Applications)

Improvix Technologies•Washington, DC

2d•$180,000 - $220,000•Remote

About The Position

Improvix Technologies is seeking a Senior Platform Engineer to build and own the application services, APIs, and enterprise integrations for a federal data and AI program. This role involves taking capabilities from design through production, including security pipelines and authorization processes, and maintaining them post-launch. It's a hands-on software engineering position that requires connecting AI services, enterprise platforms, identity, and data sources. The team includes an AI Solutions Architect, a Senior Agentic Platform Engineer, and a Technical Delivery & Integration Lead, with this role focusing on the engineering and integration aspects.

Requirements

8+ years of software engineering experience, including 3+ years building and owning production services end to end.
Strong programming ability in at least one backend language — Python, Java, Go, or TypeScript — and a track record designing RESTful APIs and services other systems depend on.
Hands-on experience integrating with enterprise platforms — ServiceNow or a comparable system — through APIs, messaging, or event streaming.
Identity integration experience: OAuth 2.0 and OIDC (or SAML), working with an enterprise identity provider such as Entra ID, Okta, or Keycloak.
Experience building and maintaining CI/CD pipelines — GitLab CI, GitHub Actions, Azure DevOps, or Jenkins — with integrated security scanning.
Hands-on ATO or equivalent compliance work: producing control evidence, hardening systems to a baseline, and remediating findings.
At least one AI or ML capability delivered to production — an LLM-backed service, a RAG feature, or a model integration — not a notebook and a demo.
Working experience deploying to AWS or Azure with infrastructure as code (Terraform, CloudFormation, or Bicep) and containers (Docker and Kubernetes or a managed equivalent).
Experience building observability into systems and supporting them in production, including incident response.
The ability to work independently on a remote team and explain a technical tradeoff to engineers and non-technical stakeholders alike.
Bachelor’s degree in a technical field, or an equivalent combination of education and directly relevant experience.
U.S. citizenship and an active Secret clearance.
Availability during East Coast business hours to stay in sync with the program.

Nice To Haves

ServiceNow-specific integration depth — IntegrationHub, MID Server, Scripted REST APIs, or Flow Designer.
Hands-on deployment in AWS GovCloud or Azure Government.
Familiarity with FedRAMP, NIST 800-53, and RMF.
Experience with model hosting services such as Amazon Bedrock or Azure OpenAI, vector databases, and LLM frameworks.
LLM evaluation or guardrail tooling experience.
Experience building tools or APIs that AI agents call, including tool-calling or Model Context Protocol (MCP) integrations.
Certifications — CompTIA Security+ or another DoD 8140 baseline, AWS or Azure developer or professional-level, or Certified Kubernetes Application Developer (CKAD).
Prior experience supporting federal or defense customers in a cleared environment.

Responsibilities

Design, build, and maintain backend services and APIs in Python, Java, Go, or TypeScript that put data and AI capabilities in front of users and systems.
Build front-end components or internal tooling where a capability needs them.
Hold the line on code quality through reviews, automated testing, and shared engineering standards.
Design services for failure: timeouts, retries, idempotency, and clear behavior when a dependency is down.
Build and maintain integrations with ServiceNow, enterprise data platforms, and internal APIs — REST, messaging, and event-driven patterns, with real error handling and monitoring.
Integrate services with enterprise identity providers using OAuth 2.0, OIDC, and SAML, including service-to-service authentication and least-privilege authorization.
Design integrations that fail loudly rather than quietly, so a broken sync is caught in minutes rather than discovered by a user weeks later.
Give the Technical Delivery & Integration Lead the technical detail behind each cross-team request, and execute the engineering work once dependencies clear.
Build and own CI/CD pipelines with automated build, test, security scanning, and deployment.
Embed security across the lifecycle: SAST and DAST, dependency and container scanning, secrets management, and least-privilege access.
Own the ATO work for your capabilities — control implementation evidence, STIG or equivalent hardening, and remediation of findings — so authorization isn’t a scramble at the end.
Turn designs from the AI Solutions Architect into production features: LLM-backed services, retrieval-augmented generation (RAG), and model API integrations.
Build the application layer around models — prompt and configuration management, evaluation hooks, guardrail enforcement, and usage and cost tracking.
Monitor AI features in production for quality, latency, cost, and safety, and tune them as usage grows.
Deploy and run your services in AWS or Azure, including their government regions, using infrastructure as code and containers on Kubernetes or managed equivalents.
Partner with the Senior Agentic Platform Engineer on the shared cloud, model-hosting, and agent infrastructure your services run on.
Build observability in from the start: logging, metrics, tracing, alerting, and dashboards someone actually looks at.
Break capabilities into deliverable increments, estimate honestly, and surface risks and dependencies while there’s still time to act on them.
Write the runbooks, technical documentation, and handoff material that let someone else support what you built.
Lead post-incident fixes for the capabilities you own — the fix that prevents the next incident, not just the one that closes the ticket.