AI Engineer

Leidos

. Build and deploy agentic AI systems capable of autonomous decision-making, tool use, and multi-step task execution .

Posted 9/16/2026full-timeWashington, DC • District of Columbia • United StatesJuniorMid-Level💰 $69,550 - $125,725 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in building and deploying agentic AI systems, with a strong focus on LLM-based features and machine learning model optimization. Proficient in evaluating AI/ML systems and collaborating with cross-functional teams to drive business decisions.

Highest-signal resume keywords

AI/ML Engineering ExperienceLLM Application DevelopmentPrompt EngineeringPython ProgrammingCloud AI Platforms

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

Machine LearningNatural Language ProcessingData PreparationModel DevelopmentModel DeploymentClassificationClusteringAnomaly DetectionText SummarizationEntity Extraction

Soft Skills

Excellent CommunicationProject ManagementAdaptability

Tools & Technologies

AWS SageMakerAzure OpenAIPyTorchKerasElasticsearchMongoDBGitLinuxTableauPower BI

Certifications & Qualifications

Public Trust Clearance

Industry Keywords

Agentic AI SystemsLLMRetrieval-Augmented GenerationEmbedding PipelinesMLOpsDistributed ComputingCloud EnvironmentsData VisualizationNoSQL DatabasesCross-Functional Collaboration

Tech Stack

Tools & technologies

AWSAzureCloudDynamoDBElasticSearchKerasLinuxMongoDBNoSQLPythonPyTorchSQLTableau

About the role

Key responsibilities & impact

Build and deploy agentic AI systems capable of autonomous decision-making, tool use, and multi-step task execution
Implement end-to-end AI/ML and GenAI projects from business needs and data preparation through model development, deployment, and monitoring
Develop LLM-based features including retrieval-augmented generation with citations, text summarization, and embedding pipelines
Design and optimize prompts for LLMs
Use LLMs such as Claude, GPT, Gemini, and Llama through APIs or cloud AI platforms
Evaluate and test GenAI features through test sets, grounding and citation checks, LLM-as-judge scoring, and production quality monitoring
Design, develop, and optimize machine learning models using Python
Deploy and manage solutions in distributed and cloud environments
Collaborate with cross-functional teams to guide business decisions

Requirements

What you’ll need

Bachelor's/Master's degree in CS, Data Science, Engineering, or Mathematics field
2+ years of hands-on AI/ML engineering experience, including demonstrable LLM application work
Experience building agentic AI systems, or strong working knowledge of agent architectures and frameworks such as LangGraph, CrewAI, Strands, or AutoGen
Working knowledge of prompt engineering, RAG, embeddings, and structured outputs
Experience in machine learning/artificial intelligence areas including classification, clustering, anomaly detection, sentiment analysis, NLP, text categorization, topic modeling, entity extraction, and text summarization
Ability to evaluate AI/ML system design, model selection, tradeoffs, and deployment considerations
Experience evaluating AI/ML systems, measuring accuracy, and catching hallucinations
Programming experience using Python and iPython notebooks
Good SQL skills
Excellent communication skills
Ability to quickly learn new tools and paradigms
Ability to work on multiple projects, meet deadlines, and manage expectations
US citizenship required
Public trust clearance required
Preferred: prompt engineering techniques including few-shot, zero-shot, and chain-of-thought prompting
Preferred: AWS or Azure and AI/ML services including AWS Bedrock, AWS SageMaker, Azure OpenAI, Azure AI Foundry, S3, and Lambda
Preferred: PyTorch or Keras
Preferred: MLOps
Preferred: Elasticsearch, Solr, or vector databases
Preferred: Git and Azure DevOps
Preferred: Linux and cloud CLI tools
Preferred: Tableau, Power BI, or other data visualization tools
Preferred: MongoDB, DynamoDB, or other distributed NoSQL databases
Preferred: full-stack systems architected for speed and distributed computing

Benefits

Comp & perks

Fully remote work arrangement
Opportunity to work on cutting-edge generative AI, agentic systems, machine learning, LLM, and prompt engineering projects