Context Engineer

Accenture Federal Services•Washington, DC

14h•$64,000 - $160,200

About The Position

The Context Engineer orchestrates delivery of AI-driven solutions by bridging business analysis, solution design, and integration so AI systems have the context needed to produce consistent, mission-aligned results. The role translates approved user stories into AFS-compliant agent prompts, designs and governs specialized subagents, operates AI build pipelines, and maintains a high-performing prompt library. It also integrates with CI/CD pipelines while upholding security and Authority to Operate (ATO) compliance standards. The role involves collaborating with AI Product Leads, Senior AI Engineers, Verification Engineers, and other cross-functional partners. The engineer documents practices, trains teammates, and supports federal clients while complying with applicable AFS, client, security, and program requirements. The work may be performed in a hybrid, remote, or client-site environment as program needs require.

Requirements

Proven experience in business analysis, solution integration, or solution design.
Strong background managing and configuring AI build pipelines with generative, agentic AI tools and agent-orchestration frameworks such as Claude, Codex, or LangGraph.
Mastery of LLM prompting and hands-on API experience, including prompt parameters, components, and conversational LLM outputs.
Familiarity with CI/CD processes and tools, including security validation and compliance integration.
Ability to query and analyze structured data and prior context files to derive actionable insights.
Understanding of AI build pipelines, subagent orchestration, and RAG knowledge-base creation.
Familiarity with version control systems and prompt-library management.
Experience working in agile environments or with Scrum methodologies.
Knowledge of security and compliance processes in AI operations.
U.S. citizenship is required.
Ability to work with federal clients and comply with applicable AFS, client, security, and program requirements.
Ability to work in a hybrid, remote, or client-site environment as program needs require.
Applicants must have U.S. work authorization that does not now or in the future require visa sponsorship.

Nice To Haves

Bachelor’s degree in Business, Information Systems, Computer Science, Engineering, or equivalent practical experience.
Basic coding skills for rapid prototyping and iterating on data and prompting workflows.
Multimodal prompting experience with images or video and familiarity with text-embedding models.
Background in copywriting, data science, or software engineering.
Prior work with conversational LLM outputs and feedback-driven prompt refinement.
Excellent written communication skills, including the ability to document work clearly and train others.

Responsibilities

Translate approved user stories into AFS-compliant agent prompts aligned with project objectives and mission outcomes.
Design specialized subagent architecture, defining each agent’s purpose, scope, tools, and boundaries.
Build routing, delegation, and escalation logic for direct responses, subagent invocation, parallel specialists, clarification, tool use, human escalation, and unsafe-action refusal.
Define agent-to-subagent contracts covering inputs, context boundaries, structured outputs, confidence and evidence, latency budgets, cancellation behavior, and failure handling.
Configure, manage, and operate AI build pipelines using generative agentic tools and orchestration frameworks such as Claude, Codex, and LangGraph.
Own and maintain the prompt library, including version control, quality scoring, and continuous improvement based on reviewed outputs and feedback.
Create and maintain RAG knowledge bases and query prior context files for precedent and predictive insight before authoring prompts.
Oversee and integrate CI/CD pipelines, including security validations such as SAST and vulnerability scanning, and automated ATO evidence generation.
Build rapid prototypes using AI-assisted coding to test complex prompting and data workflows before full implementation.
Document work and produce clear write-ups; serve as a team expert and trainer on prompting and context practices.
Collaborate with AI Product Leads, Senior AI Engineers, Verification Engineers, and other cross-functional teams to deliver AI solutions.