Senior AI/ML Engineer

Leidos•Alexandria, VA

17h

About The Position

This Department of War enterprise data and analytics program delivers mission-critical capabilities that enable leaders across the Department to make faster, better-informed decisions using trusted data at scale. Leidos Digital Modernization sector is seeking an experienced Senior AI/ML Engineer to support the delivery, enhancement, and adoption of enterprise data and analytics products used across multiple DoD organizations. In this role, you will work alongside government partners, engineers, and other industry teammates to translate operational and strategic requirements into scalable, production-ready solutions. You will contribute directly to product planning, execution, and continuous improvement—helping ensure capabilities are delivered efficiently, aligned to mission priorities, and positioned for sustained success. This position offers the opportunity to work on a high-visibility, enterprise program at the intersection of data, analytics, and emerging AI technologies. Ideal candidates are motivated by mission impact, comfortable operating in complex stakeholder environments, and interested in building deep domain expertise while delivering capabilities with real-world national security outcomes.

Requirements

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)

Nice To Haves

Active Top Secret clearance.
10+ years of experience in AI/ML and/or data intelligence engineering or related fields.
Experience operating within SAFe or large-scale Agile frameworks supporting enterprise systems.
Experience supporting AI/ML solutions across multi-enclave DoD environments.
Experience integrating AI/ML models into CI/CD and DevSecOps environments.
Experience developing and deploying AI/ML models using frameworks such as PyTorch, TensorFlow, or equivalent.
Experience working with vector databases and retrieval-augmented generation (RAG) architectures.
Experience with cloud-based AI/ML platforms (AWS, Azure, or GCP).
Experience implementing automated model validation, bias detection, and drift monitoring frameworks.
Experience integrating AI services into enterprise APIs and microservices architectures.
Experience optimizing GPU-based training and inference pipelines.
Experience supporting enterprise-scale analytics, data platforms, or AI modernization initiatives.
Experience with cloud-based AI/ML platforms and tools.
Familiarity with cybersecurity principles and practices.
Experience designing and implementing safety, guardrails, and bias-mitigation strategies for autonomous agents and multiagent systems
Experience integrating agents with cloud-native workflows, streaming data pipelines, and real-time decision-making environments
Familiarity with evaluation and observability tools for AI agents, such as LangSmith, OpenAI Evals, or custom telemetry systems
Experience with AI service integration such as NIMS, Azure OpenAI, Bedrock, GCP Vertex AI
Hands-on GPU programming experience for ML workloads using CUDA, PyTorch, or TensorFlow, including optimization for performance and efficiency.

Responsibilities

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.