The Leidos Chief Data & Analytics Office (CDAO) is a high-growth organization at the center of the company's technology strategy. Our Operational AI (Ops.AI) division is seeking a motivated and talented Principal Agentic AI Systems Engineer to join our team. This role is critical for building the next generation of intelligent agentic systems that will power our nation's most mission-critical applications.
This is an exciting opportunity for a hands-on builder who excels at implementing modern AI concepts into production-ready solutions. You will be a key technical contributor responsible for the entire lifecycle of our Agentic AI systems; from design and development to deployment, optimization, and monitoring. You will work with a team of experts to build the scalable, high-performance, and trusted AI systems that help Leidos accelerate innovation and improve mission outcomes.
Primary Responsibilities- Develop and deploy enterprise-scale agentic AI systems, including Multi-Agent and Agent-to-Agent (A2A) workflows, leveraging common industry standards, such as the Model Context Protocol (MCP), to create interoperable and scalable AI agents.
- Implement robust MCP Tools and Resources to securely expose data and functionality, enabling LLMs to interact with internal systems and APIs in a standardized way.
- Contribute to the architecture and implementation of a centralized "AI Gateway" to ensure platform independence, Large Language Model (LLM) agnostism, and provide a unified interface for leveraging various LLMs.
- Implement and manage robust observability pipelines to track trace-level data, monitor model latency, and optimize the cost and performance of Generative AI systems in production.
- Collaborate closely with principal engineers, data scientists, and systems architects to translate strategic designs into hardened, production-grade solutions.
- Implement and maintain robust AI guardrails to filter inputs and outputs, preventing data leakage (both into unsecure systems and future LLMs), prompt injection, and other adversarial attacks.
- Apply and promote software engineering best practices, including robust version control, comprehensive automated testing, and mature CI/CD processes for AI systems.
- Stay current with industry trends in agentic AI, operational AI, and MLOps to continuously evolve the team's capabilities and technical implementation.
Basic Qualifications- A Bachelor's degree in Computer Science, Engineering, or a related quantitative field with 12+ years of professional experience, or a Master's degree with 10+ years of relevant experience.
- Demonstrated programming proficiency in Python and hands-on experience with major ML libraries and frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
- Hands-on experience implementing solutions using the Model Context Protocol (MCP) to build standardized tools and data sources for LLM applications.
- Experience with software engineering best practices and tools, including version control, automated testing, and CI/CD pipelines.
- Solid understanding of the full machine learning lifecycle, from data preparation and model training to deployment and monitoring.
- A strong understanding of agentic AI patterns, multi-agent systems, and LLM-based workflows.
- An understanding of cybersecurity principles as they apply to AI systems, including threat modeling and vulnerability assessment.
Preferred Qualifications- Experience with MLOps platforms such as MLflow, Kubeflow, or AWS Sagemaker.
- Experience with containerization and orchestration technologies (e.g., Docker, Kubernetes).
- Familiarity with Infrastructure-as-Code (IaC) tools like Terraform or CloudFormation.
- Hands-on experience with a major cloud platform (AWS, Azure, or Google Cloud Platform).
- Experience working within the national security, defense, or intelligence communities.
- Knowledge of AI ethics, responsible AI practices, and federal compliance standards (e.g., NIST, CMMC).
- Knowledge of AI security frameworks such as MITRE ATLAS, and the NIST AI Risk Management Framework (AI RMF).
Contributions to open-source AI or ML projects.
At Leidos, we don't want someone who "fits the mold"-we want someone who melts it down and builds something better. This is a role for the restless, the over-caffeinated, the ones who ask, "what's next?" before the dust settles on "what's now."
If you're already scheming step 20 while everyone else is still debating step 2... good. You'll fit right in.
Original Posting:January 6, 2026
For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.
Pay Range:Pay Range $131,300.00 - $237,350.00
The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.
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