Design, develop, test, and maintain AI agents and generative AI applications using Python.
Develop agent workflows that use large language models to interpret user requests, reason across multiple steps, select appropriate tools, retrieve information, and generate grounded responses.
Design, test, and iteratively refine system prompts, task instructions, tool-use instructions, few-shot examples, response formats, and other prompt-engineering components.
Apply prompt-engineering techniques to improve response accuracy, consistency, grounding, tool selection, adherence to business rules, and overall user experience.
Develop agent orchestration logic including state management, context management, tool selection, multi-step execution, retries, exception handling, and recovery.
Develop Python application components using boto3, botocore, and other appropriate SDKs and libraries to interact with AI, data, storage, security, and supporting cloud services.
Integrate agents with approved tools, APIs, Model Context Protocol (MCP) services, databases, knowledge sources, and enterprise applications.
Work with AI Integration Engineers to define tool requirements, expected inputs and outputs, validation rules, and integration behaviors needed by AI agents.
Build and optimize retrieval-augmented generation (RAG) capabilities including query formulation, retrieval logic, context assembly, grounding, semantic search, and use of retrieved information within agent workflows.
Develop prompt and context strategies for working with structured and unstructured enterprise information while minimizing irrelevant or unsupported model responses.
Implement structured outputs, schema validation, response validation, guardrails, error handling, and other controls required for reliable enterprise AI behavior.
Develop reusable Python libraries, utilities, agent components, prompts, and development patterns that can be used across multiple AI use cases.
Create automated unit, integration, regression, and AI evaluation tests covering agent workflows, prompts, tool selection, model responses, retrieval quality, and application behavior.
Develop and maintain evaluation methods for response quality, factual grounding, hallucination, tool-use accuracy, retrieval effectiveness, latency, consistency, and regression.
Analyze model and agent behavior using logs, prompts, responses, tool calls, retrieved context, and downstream results to identify and correct performance or reliability issues.
Compare and evaluate models, prompting approaches, retrieval strategies, and agent designs based on accuracy, reliability, performance, maintainability, security, and suitability for the use case.
Collaborate with functional experts and Product Owners to translate business problems into clearly defined AI use cases, expected behaviors, acceptance criteria, and measurable outcomes.
Support demonstrations, user testing, defect resolution, production validation, monitoring, and continuous improvement of deployed AI capabilities.
Maintain source code, prompts, technical designs, configuration, evaluation criteria, support documentation, and other AI development artifacts.
Participate in code reviews, architecture discussions, backlog refinement, demonstrations, testing, release readiness, and other Agile/SAFe delivery activities.
Other duties as assigned.
Must possess an active Secret security clearance
Bachelor's degree in Computer Science, Software Engineering, Artificial Intelligence, Information Systems, Engineering, or a related field; equivalent relevant experience may be considered.
3+ years of hands-on software development experience, including strong experience developing production-quality applications and services using Python.
Strong Python development skills including object-oriented development, modules/packages, dependency management, exception handling, logging, testing, debugging, and API development.
Hands-on experience developing AI agents, agentic workflows, LLM-based applications, or comparable generative AI capabilities.
Strong hands-on prompt engineering experience, including development and refinement of system prompts, task instructions, few-shot examples, structured outputs, grounding strategies, tool-use instructions, and context-management approaches.
Experience developing applications that interact with large language models through APIs or SDKs.
Experience developing agent workflows that use tools, APIs, or MCP-based services, including tool selection, structured inputs and outputs, error handling, and integration of tool results into agent behavior.
Hands-on experience using boto3 and botocore to develop applications that interact with cloud services and APIs.
Experience with retrieval-augmented generation (RAG), semantic search, embeddings, vector search, or other knowledge-grounded AI techniques.
Experience developing REST APIs, consuming APIs, working with JSON, and integrating Python applications with external services.
Experience developing automated tests or evaluation methods for AI applications, including assessment of response quality, grounding, tool use, or regression behavior.
Experience with Git/source control, code reviews, CI/CD, debugging, logging, and modern software-development practices.
Strong troubleshooting skills and ability to diagnose problems across Python code, agent behavior, prompts, model responses, retrieval, tools, APIs, and downstream services.
Advanced experience developing agentic AI applications involving multiple tools, multi-step workflows, state management, or complex orchestration.
Experience designing and optimizing production prompt libraries, agent instructions, reusable prompt templates, and prompt-evaluation approaches.
Experience evaluating and mitigating hallucination, weak grounding, inconsistent responses, poor tool selection, excessive context, and other common LLM application problems.
Experience with Amazon Bedrock, foundation-model APIs, or comparable managed generative AI services.
Experience with agent-development frameworks such as LangGraph, LangChain, Strands Agents, or comparable orchestration technologies.
Experience building RAG applications using embeddings, vector search, semantic retrieval, hybrid retrieval, document ingestion, or reranking.
Experience with vector databases or Oracle AI Vector Search is a plus.
Experience developing AI capabilities involving case management, knowledge management, help desk support, summarization, classification, recommendation, or workflow assistance.
Experience consuming MCP services and integrating MCP tools into AI agent workflows.
Understanding of AI application security considerations including prompt injection, inappropriate tool use, excessive agency, sensitive-data exposure, authentication, authorization, and least-privilege access.
Working knowledge of SQL and relational databases, particularly Oracle.
Experience integrating AI applications with PeopleSoft HCM, PeopleSoft CRM, or other complex enterprise applications is a plus.
Experience developing containerized Python applications using Docker and deploying applications into cloud or Kubernetes environments.
Familiarity with OCI or deployment of AI applications into Oracle Cloud Infrastructure is a plus.
Experience working in Agile or SAFe environments and using Azure DevOps (ADO) or a similar lifecycle-management tool.
Experience supporting secured Federal or DoD enterprise systems is preferred.
Python, AWS, AI/ML, cloud, or related certification is a plus.
Compensation at DXC is influenced by an array of factors, including but not limited to the experience, job-related knowledge, skills, competencies, as well as contract-specific affordability and organizational requirements. A reasonable estimate of the current compensation range for this position is $73,400 - $136,300.
Full-time hires are eligible to participate in the DXC benefit program. DXC offers a comprehensive, flexible, and competitive benefits program which includes, but is not limited to, health, dental, and vision insurance coverage; employee wellness; life and disability insurance; a retirement savings plan, paid holidays, paid time off.
At DXC Technology, we believe strong connections and community are key to our success. Our work model prioritizes in-person collaboration while offering flexibility to support wellbeing, productivity, individual work styles, and life circumstances. We’re committed to fostering an inclusive environment where everyone can thrive.
