Senior AI/ML Engineer

Leidos

. Design, develop, and optimize AI and ML solutions to enhance operational and analytical capabilities within a larger enterprise level Data and AI analytics platform.

Posted 4/22/2026full-timeAlexandria • Maryland, Virginia • 🇺🇸 United StatesSenior💰 $107,900 - $195,050 per yearWebsite

Tech Stack

Tools & technologies

CloudCyber SecurityDockerJavaKubernetesMicroservicesPythonPyTorchScikit-LearnTensorflowVMware

About the role

Key responsibilities & impact

Design, develop, and optimize AI and ML solutions to enhance operational and analytical capabilities within a larger enterprise level Data and AI analytics platform.
Build and maintain end-to-end ML pipelines including data ingestion, feature engineering, model training, validation, and inference.
Train and tune algorithms to improve predictive accuracy and decision-support tasks.
Integrate AI/ML models into production environments using APIs, microservices, and DevSecOps pipelines.
Support development and maintenance of model serving infrastructure and scalable inference capabilities.
Implement model monitoring, performance evaluation, and drift detection mechanisms.
Optimize model performance, scalability, and efficiency for production workloads.
Collaborate with data engineers, data scientists, software developers, and DevSecOps teams to ensure alignment with enterprise architecture and security requirements.
Support secure development and deployment of AI/ML models in compliance with enterprise cybersecurity requirements.
Contribute to development of AI/ML artifacts including documentation, testing frameworks, and model evaluation reports.
Support integration with enterprise AI/ML platforms and external model providers (e.g., cloud-based AI services).
Ensure compliance with cybersecurity policies and standards throughout the project lifecycle.
Stay updated on industry trends and advancements in AI/ML technologies.
Identify and resolve technical challenges related to model accuracy, scalability, and integration.
Analyze system performance metrics and recommend improvements for efficiency and scalability.
Identify and integrate appropriate COTS, government, and custom tools within established frameworks.
Manage project timelines and deliverables, ensuring adherence to quality standards.
Facilitate communication between technical teams and stakeholders to align project goals.
Develop and implement best practices for model development and deployment.

Requirements

What you’ll need

Active Secret clearance
Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, or related technical discipline and 8–12 years of relevant experience OR Master’s degree in a related field and 6–10 years of relevant experience
Minimum of 6 years of experience in AI/ML and/or data intelligence engineering or related fields
Experience developing and deploying machine learning models in enterprise environments
Experience with programming languages such as Python, R, or Java and ML frameworks such as PyTorch, TensorFlow, or Scikit-learn
Experience building and maintaining ML pipelines and data processing workflows
Experience deploying models in containerized environments (Docker, Kubernetes)
Experience integrating AI/ML solutions into APIs and microservices architectures
Experience implementing model evaluation, performance tuning, and lifecycle management practices
Experience with data pipeline construction and management
Strong problem-solving abilities and analytical thinking
Strong communication and interpersonal skills
Experience in at least 2 of the following: Developing Agentic AI solutions, including autonomous planning–execution–reflection loops, multi-agent collaboration and coordination, and tool usage patterns including API integration, retrieval-augmented generation (RAG), and memory/context management
Generative AI models including prompt engineering, chain-of-thought reasoning, and Natural Language Processing (NLP) tasks such as entity extraction, summarization, and semantic search.
Large Language Models (LLMs) and agent frameworks such as LangChain, LangGraph, CrewAI, A2A, MCP, or AutoGen
Vector databases (e.g., Pinecone, Weaviate, FAISS)
Deployment into virtualized and containerized environments (e.g., VMware, Docker, Kubernetes)

Benefits

Comp & perks

Health and Wellness programs
Income Protection
Paid Leave
Retirement

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

AI solutionsML solutionsmachine learning modelsdata ingestionfeature engineeringmodel trainingmodel validationmodel inferencepredictive accuracymodel performance tuning

Soft Skills

problem-solvinganalytical thinkingcommunicationinterpersonal skillscollaborationproject managementquality assurancestakeholder alignmentbest practices implementationtechnical challenge resolution

Certifications

Active Secret clearanceBachelor’s degreeMaster’s degree