Agentic AI Developer

Booz Allen Hamilton•San Antonio, TX

1d•$99,000 - $225,000•Remote

About The Position

As an AI/ML engineer, you know that the value of AI comes from applying it to real operational challenges. We need your software engineering skills and problem-solving mindset to help a defense client turn complex data and workflows into secure, reliable AI-enabled capabilities. As an agentic AI developer on our team, you’ll build, integrate, test, deploy, and maintain AI agents and supporting applications using Booz Allen’s Agent Foundry platform in an AWS environment. In this role, you’ll contribute throughout the development lifecycle, connecting agents to enterprise data and tools, implementing retrieval and workflow orchestration, integrating ML capabilities, and evaluating application performance and reliability. You’ll collaborate with AI/ML engineers, data and cloud engineers, solutions architects, platform developers, and domain subject matter experts to translate mission needs into working software. Your contributions may support mission readiness assessments, capability gap analysis, knowledge discovery, workflow automation, and computer vision or geospatial analysis for infrastructure and asset management. You’ll take responsibility for assigned features, contribute to technical design discussions and code reviews, and work with teammates to troubleshoot issues and improve solutions. Building on capabilities provided by the platform team, you’ll help deliver mission-focused agents with appropriate security controls, traceable outputs, and human oversight. You’ll have opportunities to apply your technical strengths while expanding your experience across agentic AI, ML, and cloud software engineering. Work with us to turn advances in AI into dependable capabilities that help defense organizations make informed decisions and accomplish their missions. Join us. The world can’t wait.

Requirements

2+ years of experience developing production-grade Python software, including back-end applications, APIs, or data services
2+ years of experience developing, integrating, or deploying AI/ML-enabled applications, including hands-on implementation of applications using large language models (LLMs), retrieval-augmented generation, or agentic workflows
Experience contributing to a software development project using Git-based version control, code reviews, automated testing, and CI/CD
Experience evaluating an AI/ML application using test datasets, performance metrics, error analysis, or documented user feedback to identify and implement improvements
Knowledge of LLM and agent development patterns, including tool or function calling, context management, retrieval, workflow orchestration, and human-in-the-loop controls
Knowledge of AWS fundamentals, including identity and access management (IAM), compute, storage, networking, and managed AI/ML services, and secure development practices such as least-privilege access, secrets management, and input and output validation
Ability to translate a mission use case into an implemented and tested software feature and communicate its design, results, and limitations to engineering teammates and domain subject matter experts
Ability to obtain a Secret clearance
Bachelor’s degree in CS, Software Engineering, Data Science, or Engineering and 5+ years of experience in software engineering, AI/ML engineering, data engineering, or cloud engineering, or Master’s degree in CS, Software Engineering, Data Science, or Engineering and 2+ years of experience in software engineering, AI/ML engineering, data engineering, or cloud engineering

Nice To Haves

Experience developing or deploying AI-enabled applications on AWS using services such as Amazon Bedrock, Amazon SageMaker AI, AWS Lambda, Amazon S3, Amazon DynamoDB, Amazon OpenSearch Service, Amazon ECS, or Amazon EKS
Experience implementing agent orchestration, tool integration, or knowledge retrieval using Strands Agents, LangGraph, LangChain, Model Context Protocol, Temporal, vector databases, or knowledge graphs
Experience building and operating production data pipelines, including ETL/ELT, batch or streaming ingestion, data quality validation, schema management, metadata, and lineage, using technologies such as SQL, Python, AWS Glue, Apache Spark, Databricks, or Apache Airflow
Experience implementing AWS DevOps or MLOps practices, including automated deployments, containerization, Infrastructure-as-Code, observability, and troubleshooting, using technologies such as Docker, Kubernetes, Terraform, AWS CDK, GitHub Actions, Amazon CloudWatch, or OpenTelemetry
Experience securing AWS-hosted models, inference endpoints, or agentic applications through least-privilege IAM, service authentication and authorization, secrets management, encryption, network isolation, and audit logging, including safeguards against prompt injection, sensitive-data exposure, and unauthorized tool execution
Experience developing computer vision, multimodal, or geospatial solutions for image classification, object detection, segmentation, change detection, or asset inspection, using technologies such as PyTorch, TensorFlow, OpenCV, GDAL, Rasterio, GeoPandas, or Open3D, and processing georeferenced imagery, LiDAR, photogrammetry, or point clouds
Knowledge of software and AI/ML delivery requirements in AWS GovCloud or other regulated, restricted, or classified environments, including RMF, STIG-aligned development, vulnerability remediation, or authorization-to-operate evidence
Secret clearance
AWS Professional-level, AWS Certified Security – Specialty, AWS Certified SysOps Administrator – Associate, or AWS Associate-level Certification such as AWS Certified Solutions Architect – Associate, AWS Certified Developer – Associate, AWS Certified CloudOps Engineer – Associate, AWS Certified Data Engineer – Associate, or AWS Certified Machine Learning Engineer – Associate Certification
CompTIA Security+ Certification

Responsibilities

Build, integrate, test, deploy, and maintain AI agents and supporting applications using Booz Allen’s Agent Foundry platform in an AWS environment.
Connect agents to enterprise data and tools.
Implement retrieval and workflow orchestration.
Integrate ML capabilities.
Evaluate application performance and reliability.
Collaborate with AI/ML engineers, data and cloud engineers, solutions architects, platform developers, and domain subject matter experts to translate mission needs into working software.
Support mission readiness assessments, capability gap analysis, knowledge discovery, workflow automation, and computer vision or geospatial analysis for infrastructure and asset management.
Take responsibility for assigned features.
Contribute to technical design discussions and code reviews.
Work with teammates to troubleshoot issues and improve solutions.
Deliver mission-focused agents with appropriate security controls, traceable outputs, and human oversight.
Apply technical strengths while expanding experience across agentic AI, ML, and cloud software engineering.