AI Engineer (Agentic Workflows / AI Workbench)

DXC Technology•Washington, DC

2d•Hybrid

About The Position

DXC Technology (NYSE: DXC) helps global companies run their mission-critical systems and operations while modernizing IT, optimizing data architectures, and ensuring security and scalability across public, private, and hybrid clouds. The world’s largest companies and public sector organizations trust DXC to deploy services across the Enterprise Technology Stack to drive new levels of performance, competitiveness, and customer experience. Learn more at DXC.com. Location: Hybrid – Washington, DC. Candidates within 25 miles of a DXC office are required to work on-site 2 days per week. Overview: The AI Engineer implements and operationalizes agentic AI solutions using AI Workbench. This role builds intelligent-agent workflows, integrates enterprise tools and data sources, implements RAG pipelines, and partners with DevOps/DataOps and QA to deliver reliable, secure, and observable AI capabilities.

Requirements

4+ years of software engineering experience, including Python.
Experience with API development/integration and microservices.
Familiarity with containerized environments and basic cloud concepts.
Strong problem-solving skills and ability to work in Agile teams.
Secret Clearance and U.S. Citizenship.
5+ years of experience in database administration and management.

Nice To Haves

Hands-on experience with GenAI/LLMs, prompt engineering, and RAG.
Experience with vector databases (e.g., pgvector, Pinecone-like concepts, FAISS) and embeddings.
Understanding of model evaluation, safety guardrails, and responsible AI practices.
Experience with orchestration/agent frameworks and tool calling patterns.

Responsibilities

Develop and configure AI agents: goals, tools, policies, conversation flows, and lifecycle management within AI Workbench.
Implement RAG pipelines: ingestion, preprocessing, chunking, embeddings generation, indexing, retrieval logic, and grounding.
Integrate agents with enterprise systems via APIs/connectors, and implement safe tool-execution patterns.
Collaborate with the Solution Architect on design choices for security, scalability, observability, and cost optimization.
Implement evaluation approaches: regression tests for prompts, accuracy checks for retrieval, and monitoring for drift and hallucination risk.
Contribute to CI/CD pipelines for agent configurations, prompt libraries, and deployment automation.
Document solutions, runbooks, and operational considerations to support hypercare and ongoing maintenance.
Troubleshoot production issues and tune performance (latency, throughput, retrieval quality).