AI Engineer

Accenture Federal Services•San Diego, CA

8d•$115,300 - $160,100

About The Position

We are looking for an AI Engineer to help drive the integration of AI-assisted development workflows into a large-scale enterprise Linux environment. This role is focused on practical AI application within software engineering, including code intelligence, developer tooling, requirements-driven development, and AI-assisted modernization of a large legacy codebase consisting primarily of modern languages and frameworks. The ideal candidate has hands-on experience integrating and operationalizing modern AI/LLM technologies within real-world development environments.

Requirements

4 years of software development with experience in any of the following:
Strong Linux experience (Red Hat / Enterprise Linux required)
Experience with LLMs, RAG systems, embeddings, or AI-assisted tooling
Experience with GitLab workflows and CI/CD
Strong Python development experience
Experience with APIs, backend systems, and automation
Strong understanding of software architecture and large-scale systems
Active Secret security clearance is required, and must be willing and able to upgrade to TS/SCI.

Nice To Haves

GPU infrastructure experience
Experience deploying or operating local/open-source models
Experience with vector databases and semantic search
Experience with developer tooling or IDE integrations
Experience with AI evaluation/testing frameworks
Experience with secure/offline AI environments
Familiarity with code indexing and static analysis systems
Ability to work independently and drive technical initiatives
Strong debugging and problem-solving skills
Experience working with legacy systems and modernization efforts

Responsibilities

Lead the design and delivery of localization strategies, air-gapped environments, and on-premises localized solutions to meet client requirements.
Build and optimize high-performance inference pipelines using technologies such as vLLM and modern GPU-serving frameworks.
Develop agentic AI workflows leveraging orchestration frameworks, tool calling, and retrieval-augmented generation (RAG).
Integrate LLMs, vector databases, APIs, and enterprise systems into production applications.
Collaborate with software engineers, and platform teams to productionize AI prototypes and services.
Improve model performance, throughput, latency, scalability, and operational efficiency.
Implement monitoring, observability, evaluation, and testing frameworks for AI systems.
Develop APIs, backend services, and automation workflows supporting AI-driven products.
Evaluate emerging AI technologies, frameworks, and developer tooling including Open Code, OpenwebUI, Claude Code and modern AI coding assistants.
Contribute to AI platform architecture, deployment standards, and engineering best practices.