Full Stack Engineer

Peraton•Home, OH

1d•$86,000 - $138,000•Onsite

About The Position

Peraton is seeking a talented and motivated Full Stack Engineer specializing in Agentic AI and External Integrations to join a dynamic team building and operating a cutting-edge Agentic AI platform. In this role, you will be at the forefront of designing, developing, and maintaining the integrations and intelligent agent capabilities that power a next-generation decision-support platform — spanning delivery, infrastructure, runtime operations, and platform health. You will work across the full stack to connect AI agents with diverse external data sources, package real-world capabilities as reliable agent tools, and ensure that the systems you build are secure, scalable, and production-ready. If you are passionate about applied AI, thrive in fast-moving environments, and take pride in building platforms that real users depend on, this is an opportunity to make a meaningful impact supporting critical missions.

Requirements

Minimum of a BS degree with 5 years of experience, MS degree with 3 years, or PhD with meaningful exposure to AI/ML systems or LLM-based products
Hands-on experience building with AI agents — multi-step reasoning, tool use, RAG pipelines, or autonomous task execution
Strong Python skills (3.12+); comfort with async/await patterns, type hints, and modern Python tooling
Familiarity with agentic frameworks and an understanding of the underlying concepts (chains, tool calling, agent loops) that transfer across tools
Comfort operating with some ambiguity in a fast-moving environment
US Citizenship is required
Ability to obtain a Public Trust

Nice To Haves

Production experience with agent frameworks (LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, OpenAI Agents SDK, Anthropic tool-use, or comparable) and a sound point of view on when each is the right tool
Experience designing and operating retrieval-augmented generation (RAG) systems — chunking strategies, embedding models, vector stores, hybrid retrieval, reranking, and grounding/citation patterns
Experience integrating with knowledge graphs, document stores, or curated content repositories as agent-accessible sources
Experience evaluating agent and LLM systems — building eval harnesses, golden datasets, regression testing, and observability for non-deterministic systems
Exposure to orchestration patterns: supervisor agents, parallel tool calls, human-in-the-loop flows, DAG-based pipeline execution
Experience building plugin or extension systems: dynamic code loading, container isolation, API mixin patterns
Familiarity with prompt engineering, evaluation frameworks, or agent observability
Familiarity with federal compliance environments: FedRAMP, FIPS 140-2/3, IronBank container hardening, OPA policy enforcement, or Section 508 accessibility
Experience with observability tooling: OpenTelemetry, Jaeger, Prometheus, Grafana, or similar distributed tracing/metrics stacks
Experience with container orchestration (Docker SDK, Kubernetes) and distributed storage (S3, MinIO, JuiceFS)
Prior work building internal tooling, enterprise automation products, or platforms for government customers

Responsibilities

Design and build integrations to external sources of information, including public APIs, licensed data feeds, partner systems, customer enterprise systems, web content, document repositories, and structured databases
Package external capabilities as agent tools and skills with clean interfaces, predictable inputs/outputs, sound error handling, and documentation usable by both AI agents and human configurators
Develop and maintain full-stack components spanning FastAPI/Python backends, React frontends, Docker containerization, and PostgreSQL
Build and consume web APIs (REST, GraphQL, or comparable), handling production integration concerns such as authentication, pagination, rate limiting, retry/backoff, schema mapping, deduplication, and caching
Implement and maintain workflow and task orchestration pipelines using systems such as Airflow, Prefect, Celery, or comparable agent orchestration patterns
Integrate LLMs into production or near-production applications, leveraging APIs such as OpenAI, Anthropic, or AWS Bedrock
Apply security best practices specific to agentic and external integration work — including secrets management, OAuth and API-key handling, defensive parsing, and prompt-injection awareness
Contribute to the build-out and operation of the Agentic AI platform across delivery, infrastructure, runtime operations, and platform health
Collaborate across teams with a product mindset, keeping the end-user experience central to technical decisions
Collaborates with stakeholders on workflows