AI Developer

What matters most

• Production agent workflows

  The role calls for Python agents that interpret requests, select tools, manage state and context, and recover from failures. Prepare to explain design choices for a multi-step workflow, including retries and exception handling.

• Grounded enterprise responses

  The job emphasizes RAG across structured and unstructured information, including query formulation, retrieval, context assembly, and prompt refinement. Prepare to explain how retrieved evidence informs an answer and how unsupported responses are limited.

• Controlled tool integration

  Agents must connect to approved APIs, MCP services, databases, and enterprise applications, with defined inputs, outputs, and validation rules. Prepare to discuss tool contracts and how invalid or failed tool results affect agent behavior.

• Evaluation and troubleshooting

  Responsibilities include automated tests and evaluation of grounding, hallucination, tool accuracy, retrieval effectiveness, latency, and regressions. Prepare to describe how logs, prompts, tool calls, and retrieved context can isolate a failure.

• Delivery with enterprise teams

  The developer works with integration, platform, data, functional, and cybersecurity teams and supports user testing, production validation, monitoring, and Agile/SAFe activities. Prepare to explain how a business use case becomes expected behavior and measurable acceptance criteria.

Likely challenges to think through

• Agent autonomy versus secure operation

  A likely challenge, not a confirmed employer problem, is allowing useful multi-step tool use without inappropriate actions or sensitive-data exposure. Prepare to discuss approved-tool boundaries, least privilege, validation, and recovery when a tool call is rejected.

• Grounding versus response time

  A likely trade-off is retrieving enough relevant enterprise context to support an answer without adding excessive or irrelevant context and latency. Prepare to compare retrieval and context-selection approaches using grounding, relevance, and latency measures.

• Variable model behavior versus reliable releases

  A likely difficulty is detecting regressions when prompts, models, retrieval, or tools change and responses are not always identical. Prepare to describe repeatable test cases, evaluation criteria, and diagnostic evidence for investigating changed behavior.

Topics to prepare

• Agent orchestration and recovery

  The posting calls for multi-step tool use, state and context management, retries, and recovery. Sketch a request-to-response workflow and mark what state persists, when a tool is selected, and how a failed call is handled.

• Prompt design and grounded responses

  System prompts, task instructions, few-shot examples, structured outputs, and grounding are explicit responsibilities. Revise a sample instruction to constrain tool use and unsupported claims, then define examples that test whether it follows those rules.

• RAG and enterprise information

  The role calls for query formulation, semantic retrieval, context assembly, and use of structured and unstructured information. Trace how a question becomes a query, retrieved context, and an answer; identify where irrelevant context could weaken grounding.

• Validation and secure tool behavior

  Schema validation and guardrails are responsibilities; prompt injection, excessive agency, and least-privilege access appear among desired skills. As a preparation exercise, specify allowed tool inputs and outputs, validation checks, and the response to an instruction embedded in retrieved content.

• AI evaluation and trade-offs

  The posting specifies evaluation of factual grounding, hallucination, tool accuracy, retrieval effectiveness, latency, consistency, and regressions. Draft a small evaluation matrix with an observable pass criterion for each of three failure modes, then explain a possible accuracy-versus-latency trade-off.

Skills to revise

• Production Python service development

  Production-quality Python, modules and packages, exception handling, logging, testing, and API development are required. Practice packaging a small service with a JSON endpoint, input validation, a handled failure path, and a unit test.

• Cloud SDK calls with boto3 and botocore

  Hands-on boto3 and botocore use is required. Practice writing a Python wrapper around an SDK call that handles service exceptions and records diagnostic information without logging sensitive request content; use a stubbed response for testing.

• REST and agent tool integration

  The role requires REST, JSON, external-service integration, and tool-using workflows; MCP-based services are also named. Practice defining a tool’s input and output schemas, consuming a mocked API response, and handling invalid JSON or a failed call before passing results to an agent.

• Retrieval and context assembly

  RAG or related knowledge-grounded techniques are required. On a small practice document set, formulate two query variants, inspect which passages are retrieved, and assemble a context that supports a cited answer without adding unsupported details.

• Automated AI tests and diagnosis

  The posting requires AI tests or evaluation and troubleshooting across prompts, retrieval, tools, and downstream services. Create test cases for correct tool choice, unsupported answers, and retrieval misses; inspect recorded prompts, tool calls, and retrieved context to explain each failure.

• Source control and delivery checks

  Git, code reviews, and CI/CD are listed requirements. Practice reviewing a small Python change for error handling and tests, then specify which unit and regression checks should run before merging it.

• Build a requirements evidence sheet

  Prepare concise examples for the stated Python, LLM application, prompt engineering, RAG, boto3/botocore, REST/JSON, testing, and CI/CD requirements. Note how you would accurately answer questions about the degree-or-equivalent, 3+ years of development, U.S. citizenship, and active Secret clearance requirements.

• Prepare a Python project walkthrough

  Choose a real project, if available, and outline its architecture, your contributions, API or SDK integrations, error handling, logging, tests, and production lessons. Clearly label any comparable rather than directly matching experience.

• Sketch a hypothetical agent workflow

  Draw a PeopleSoft HCM support scenario as a design exercise, not a claimed past project. Show tool selection, state and context management, validated inputs and outputs, retries, recovery, and boundaries around approved APIs or MCP services.

• Draft a grounded-response example

  Prepare a sample prompt, structured response format, and RAG flow covering query formulation, retrieval, context assembly, and citation or evidence checks. Explain how the design would limit unsupported answers from structured and unstructured information.

• Create an evaluation plan

  Bring a small test matrix for unit, integration, regression, and AI evaluations. Include cases for grounding, hallucination, tool-use accuracy, retrieval effectiveness, latency, and failures; identify what logs and retrieved context would help diagnose defects.

• Prepare a security and collaboration discussion

  Outline how you would address prompt injection, sensitive data, inappropriate tool use, and least-privilege access while coordinating tool contracts and validation with integration, platform, data, functional, and cybersecurity teams.

• Write focused employer questions

  Ask which PeopleSoft HCM use cases are prioritized, how functional experts define acceptance criteria, what tools and knowledge sources are approved, how production AI behavior is evaluated, and what onsite/hybrid schedule the contract requires.