AI Engineer

Leidos

About The Position

At Leidos, you'll contribute to AI solutions that serve critical national and global missions—ranging from defense and intelligence to healthcare, energy, and space exploration. Our work emphasizes Trusted Mission AI: systems that are transparent, ethical, resilient, and accountable. You’ll collaborate with multidisciplinary teams to transition AI research into operational environments where accuracy, security, and reliability are non-negotiable. Joining Leidos means applying your expertise to solve some of the most complex and meaningful challenges of our time. We are looking for a motivated AI engineer who wants to work on challenging problems in a variety of domains – including enterprise IT, health, defense, intelligence, and energy – to get results that apply and go beyond the state of the art for measurably better outcomes. We apply our knowledge, capabilities, and experience to develop and deploy Trusted Mission AI – AI that deserves to be trusted by system owners, end users, and the public – to be helpful, harmless, and honest. We are looking for a researcher that is expert in envisioning, developing, and securing AI agents using generative AI and LLM-based tools to transform and add value to human workflows.

Requirements

Self-starter with a high degree of intellectual curiosity
Proficiency in applying technical principles, theories, and concepts in the field
Experience 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
Solid understanding and hands-on experience with generative AI models including prompt engineering, chain-of-thought reasoning, and Natural Language Processing (NLP) tasks such as entity extraction, summarization, and semantic search.
Ability to effectively guide junior engineers in applying standard practices and resolving problems of moderate complexity
Working knowledge of Large Language Models (LLMs) and agent frameworks such as LangChain, LangGraph, CrewAI, A2A, MCP, or AutoGen
Experience using vector databases (e.g., Pinecone, Weaviate, FAISS)
Proficiency in modern software languages—preferably, Python
Experience with the Software Development Lifecycle (SDLC), including DevSecOps practices
Familiarity with deployment into virtualized and containerized environments (e.g., VMware, Docker, Kubernetes)
US citizenship is required and able to obtain a Secret clearance

Nice To Haves

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
Proficiency in scripting with Linux Bash, PowerShell, or equivalent automation tools
Hands-on GPU programming experience for ML workloads using CUDA, PyTorch, or TensorFlow, including optimization for performance and efficiency.

Responsibilities

Collaborate with Agentic AI Scientists to build and deploy AI agents to both automate and optimize labor intensive workflows, as well as empowering the human workforce to discover entirely new capabilities.
Support multiple programs with both R&D and customer-facing goals, to speed the transition of novel applied research and solutions development into impact on contract.
Create software to support AI agent communication, connecting models and agents to external services via API calls, testing and debugging tasks, deploying into target environments, setting up monitoring, and ensuring reliable execution of agentic AI systems.
Utilize a combination of open-source models, agentic tools, and large proprietary commercial models.
Develop novel approaches to securing agentic workflows and to evaluating the results for accuracy, performance, and impact.
Ensure AI systems adhere to ethical guidelines, transparency, and fairness principles.