Agentic AI Developer

What matters most

• Build autonomous and multi-agent workflows

  The role designs agents that reason, plan, and execute tasks, and orchestrates multi-agent workflows. Prepare to explain how you would divide work among agents and decide when an agent should stop or hand off.

• Ground agents in data and tools

  The posting calls for LLM and RAG applications connected to APIs, databases, enterprise systems, and external services. Prepare to walk through a retrieval and tool-use design, including how you would handle missing or conflicting information.

• Make agent behavior visible and assessable

  Responsibilities include observability, tracing, evaluation, and optimization of prompts, memory, context, and decisions; Langfuse is named in the qualifications. Prepare to describe what you would trace and how you would use evaluations to diagnose failures.

• Provide usable human oversight

  The role builds conversational and human-in-the-loop interfaces, and names Chainlit experience. Prepare to distinguish actions an agent could complete independently from those that should pause for human review.

• Deliver maintainable AI applications

  The posting calls for Docker-based deployment, GitLab source control and CI/CD, and collaboration with engineers, cybersecurity teams, researchers, and stakeholders. Prepare to explain how an agent prototype would become a testable, deployable application with clear team handoffs.

Likely challenges to think through

• Autonomy versus human control

  A likely design challenge is allowing agents to complete operational tasks with minimal intervention while retaining meaningful human review. Prepare a reasoned example of approval points, action limits, and what happens when an agent is uncertain; these are preparation considerations, not stated employer policies.

• Reliable decisions across retrieval and integrations

  Combining RAG, memory, and external tools may make it difficult to identify whether a poor result came from retrieved content, context handling, or an agent decision. Prepare a troubleshooting approach that uses traces and targeted evaluations to isolate each source of failure.

• Rapid prototyping versus dependable deployment

  The posting asks for research and rapid prototyping alongside deployment, CI/CD, and scalable capabilities. A likely trade-off is deciding when an emerging technique is mature enough to deploy. Prepare criteria for comparing a prototype with a simpler baseline before adding operational complexity.

Topics to prepare

• Agent orchestration and multi-agent design

  Sketch a workflow in which an agent plans a task, delegates a step, handles a failed step, and decides when to stop or request human input. Explain the trade-offs against a single-agent design. The posting calls for autonomous agents and multi-agent workflows; multi-agent design experience is preferred.

• RAG and vector retrieval

  Diagram how documents become embeddings, enter a vector database, and are retrieved for an LLM response. Identify where irrelevant retrieval could affect the answer. RAG understanding and vector-database familiarity are required; embedding experience is preferred.

• Prompts, memory, and context

  Compare two prompt and context-handling approaches for a multi-turn operational task, explaining what information each retains and when a decision could go wrong. The role explicitly involves optimizing prompts, memory, context, and agent decisions.

• Evaluation, tracing, and AI safety

  Define a small set of agent test cases and explain which traces and outcomes would reveal a wrong tool choice or an unsupported answer. Observability and evaluation pipelines are responsibilities; AI safety and governance knowledge is preferred.

• Agent integration and human oversight

  Draw the data flow between an agent, a REST API, a database, and a human approval point; mark where errors should be surfaced. The posting calls for enterprise integrations, modern backend familiarity, and human-in-the-loop interfaces.

Skills to revise

• Build a Python and LangChain agent

  As a revision exercise, implement a small Python agent with LangChain that uses an LLM API and a defined stopping condition; walk through one successful and one failed run. Python, LLM application development, and LangChain are required in the posting.

• Connect an agent to a REST API

  Practice writing a Python integration that calls a REST endpoint, validates its response, and handles an error without treating the result as a successful action. API integration is a stated responsibility, and REST familiarity is required.

• Create a Chainlit conversation

  Build a short Chainlit interaction that displays an agent response and pauses for human input before a consequential next step. Chainlit experience is required, and conversational and human-in-the-loop interfaces are responsibilities.

• Trace and evaluate with Langfuse

  Instrument a sample agent run in Langfuse, inspect its steps, and compare two runs against the same test case. The posting requires experience with observability and tracing tools such as Langfuse and calls for evaluation pipelines.

• Containerize an AI application

  Write a Dockerfile for the sample application, run it locally, and check that configuration is supplied at runtime rather than embedded in the image. Docker experience and Docker-based deployment are explicitly listed.

• Maintain a GitLab CI/CD workflow

  Revise a small GitLab repository workflow by adding a pipeline that runs a Python test and builds the Docker image; explain what happens when either stage fails. The posting requires GitLab repository and CI/CD experience.

• Map the required qualifications to evidence

  Make a one-page inventory of examples you can substantiate for Python, software development, LLM applications, LangChain, Langfuse, Chainlit, Docker, GitLab, REST APIs, vector databases, and RAG. Note the posting’s degree-or-equivalent-experience and 3+ years requirements without assuming you meet them.

• Rehearse an agent project walkthrough

  If you have a relevant project, prepare a concise account of its goal, your contribution, architecture, agent decisions, integrations, results, and tradeoffs. Distinguish work you built from work you would propose.

• Sketch a multi-agent RAG design

  As a practice exercise, draw a Python-based workflow that retrieves from a vector database, uses an LLM to plan and call a REST API, and routes consequential actions through a human-in-the-loop interface. Explain where orchestration, prompts, memory, and context handling can fail.

• Prepare an evaluation and tracing plan

  Draft sample tests for retrieval quality, agent decisions, tool-call failures, and human handoffs. Show what you would trace with a tool such as Langfuse and how findings would guide changes to prompts or workflow behavior.

• Explain a deployment path

  Prepare a simple Docker and GitLab repository and CI/CD outline for the practice design. Be ready to explain testing, configuration, and deployment choices; the posting names these tools but does not prescribe a particular pipeline.

• Select collaboration and safety examples

  Prepare brief, factual examples, if available, of resolving an engineering problem with stakeholders or cybersecurity colleagues. Separately, identify safety or governance tradeoffs you would discuss for an autonomous system; those topics appear among the preferred qualifications.

• Write targeted questions for Leidos

  Ask what operational tasks and data sources the team prioritizes, how it evaluates agent performance and human oversight, and what deployment constraints apply. Clarify the on-site expectations and the posting’s Top Secret/SCI with Polygraph designation versus its preferred clearance-or-eligibility wording.