AI Engineer

Summary

Leidos is an industry and technology leader serving government and commercial customers with digital and mission innovations. The AI Engineer will design, develop, deploy, and monitor generative AI, agentic systems, machine learning, and LLM solutions in distributed and cloud environments. The role also involves prompt engineering, evaluation of GenAI features, model optimization, and collaboration with cross-functional teams.

Responsibilities

• 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 understanding business needs to data preparation, model development,deploymentand monitoring
• Develop LLM-based features such as retrieval-augmented generation (RAG) with citations, text summarization, and embedding pipelines
• Design andoptimizeprompts using prompt engineering techniques for LLMs to achieve desired outcomes
• Work with Large Language Models (LLMs) such as Claude, GPT, Gemini, Llama, etc. via APIs or cloud AI platforms to develop solutions for specific tasks
• Evaluate and test GenAI features: building test sets, grounding and citation checks, LLM-as-judge scoring, and production quality monitoring
• Design, develop, andoptimizemachine learning models using Python
• Deploy and manage solutions in distributed and cloud environments
• Collaborate with cross-functional teams to guide business decisions

Skills

• • Bachelor's/Master'sdegree 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 (agents with tool/function calling, planning or task decomposition, and multi-step execution), or strong working knowledge of agent architectures and frameworks such asLangGraph,CrewAI, Strands, orAutoGen
• • Working knowledge of the modern LLM stack: prompt engineering, RAG, embeddings, and structured outputs
• • Experience in one or more areas of machine learning / artificial intelligence such as classification, clustering, anomaly detection, sentiment analysis, and NLP problems such as text categorization, topic modeling, entity extraction, and text summarization
• • Ability to think critically about AI or ML system design, including model selection, tradeoffs, and real-world deployment considerations
• • Experience evaluating AI/ML systems: testing, measuring accuracy, and catching hallucinations
• • Programming experience using Python andiPythonnotebooks; good SQL skills
• • Excellent communication skills to communicate with wide technical and business users
• • Demonstrate ability to quickly learn new tools and paradigms to deploycutting edgesolutions
• • Adept at simultaneously working on multiple projects, meeting deadlines, and managing expectations
• • Experience with prompt engineering techniques such as few-shot learning, zero-shot learning, and chain-of-thought prompting
• • Experience with cloud platforms (AWS or Azure) and their AI/ML services such as AWS Bedrock, AWS SageMaker, Azure OpenAI, or Azure AI Foundry, and core services such as S3 and Lambda functions
• • Experience in using deep learning frameworks such asPyTorchorKeras, etc
• • Experience inMLOpsto operationalize the model building process and monitor models in production
• • Familiarity with search and vector retrieval such as Elasticsearch,Solr, or vector databases
• • Familiarity with version control systems, specifically Git, and experience with platforms like Azure DevOps
• • Familiarity with Linux and cloud CLI tools
• • Experience creating interactive data visualizations and dashboards in Tableau, Power BI, or other tools
• • Experience with distributed NoSQL databases such as MongoDB, DynamoDB, etc
• • Ability to build full stack systems architected for speed and distributed computing

Qualifications

Must Haves

• • Bachelor's/Master'sdegree 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 (agents with tool/function calling, planning or task decomposition, and multi-step execution), or strong working knowledge of agent architectures and frameworks such asLangGraph,CrewAI, Strands, orAutoGen
• • Working knowledge of the modern LLM stack: prompt engineering, RAG, embeddings, and structured outputs
• • Experience in one or more areas of machine learning / artificial intelligence such as classification, clustering, anomaly detection, sentiment analysis, and NLP problems such as text categorization, topic modeling, entity extraction, and text summarization
• • Ability to think critically about AI or ML system design, including model selection, tradeoffs, and real-world deployment considerations
• • Experience evaluating AI/ML systems: testing, measuring accuracy, and catching hallucinations
• • Programming experience using Python andiPythonnotebooks; good SQL skills
• • Excellent communication skills to communicate with wide technical and business users
• • Demonstrate ability to quickly learn new tools and paradigms to deploycutting edgesolutions
• • Adept at simultaneously working on multiple projects, meeting deadlines, and managing expectations

Nice to Haves

• • Experience with prompt engineering techniques such as few-shot learning, zero-shot learning, and chain-of-thought prompting
• • Experience with cloud platforms (AWS or Azure) and their AI/ML services such as AWS Bedrock, AWS SageMaker, Azure OpenAI, or Azure AI Foundry, and core services such as S3 and Lambda functions
• • Experience in using deep learning frameworks such asPyTorchorKeras, etc
• • Experience inMLOpsto operationalize the model building process and monitor models in production
• • Familiarity with search and vector retrieval such as Elasticsearch,Solr, or vector databases
• • Familiarity with version control systems, specifically Git, and experience with platforms like Azure DevOps
• • Familiarity with Linux and cloud CLI tools
• • Experience creating interactive data visualizations and dashboards in Tableau, Power BI, or other tools
• • Experience with distributed NoSQL databases such as MongoDB, DynamoDB, etc
• • Ability to build full stack systems architected for speed and distributed computing

Benefits

• This role is fully remote.
• Health and Wellness programs
• Income Protection
• Paid Leave
• Retirement