Senior 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 Senior 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 accurate, ethical, reliable, and adaptable. 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. Primary Responsibilities The Senior AI Engineer will 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. As a member of the Leidos AI Accelerator, they will be tasked at different times with both R&D as well as customer-facing goals, to speed the transition of novel applied research and solutions development into impact on contract. The tasks of the Senior AI Engineer will include writing software code to support AI agent communication, connecting models and agents to external services via API calls, support testing and debugging tasks, deployment into target environments, setting up monitoring, and ensuring reliable execution of agentic AI systems. They will utilize a combination of open source models, agentic tools, and large proprietary commercial models. They will be developing novel approaches to securing agentic workflows and to evaluating the results for accuracy, performance, and impact. They will be expected to ensure AI systems adhere to ethical guidelines, transparency, and fairness principles. They should expect they may conduct research, develop prototypes, evaluate and document results, potentially through publication and presentation at conferences and other public forums. They should also expect they may be part of a team developing solutions for deployment into operational environments, or for integration into mission systems. They should be a self-starter while also working well within the team, collaborating and sharing discoveries and seeking feedback. Multiple openings from Mid to Senior levels. The various position’s minimum education and experience requirements are as follows: T3: Bachelor's degree in Computer Science, Engineering or related field and 4+ years of relevant experience, or a Masters degree with 2+ years of experience T4: Bachelor's degree in Computer Science, Engineering or related field and 8+ years of relevant experience, or a Masters degree with 6+ years of experience T5: Bachelor's degree in Computer Science, Engineering or related field and 12+ years of relevant experience, or a Masters degree with 10+ years of experience T6: Master’s degree in Computer Science, Engineering or related field and 15+ years of relevant experience, or a Doctorate degree with 13+ years of experience

Requirements

Practical experience with Large Language Models (LLMs) and agent frameworks such as LangChain, LangGraph, CrewAI, A2A, MCP, or AutoGen
Ability to design and implement tool-using AI agents, including API integration, retrieval-augmented generation (RAG), and memory/context management
Experience employing vector databases (Pinecone, Weaviate, FAISS)
Strong communication skills with the ability to present complex AI outputs in an accessible manner to non-technical stakeholders
Experience working with end users to streamline workflows and produce applied solutions
Experience with the Software Development Lifecycle (SDLC), including DevSecOps practices
Experience deploying into virtualized and containerized environments (e.g., VMware, Docker, Kubernetes)
Self-starter with a high degree of intellectual curiosity
Proficiency in modern software language such as Python and experience developing modular, production-grade AI pipelines
Ability to obtain a Secret clearance
Bachelor's degree in Computer Science, Engineering or related field and 4+ years of relevant experience, or a Masters degree with 2+ years of experience
Bachelor's degree in Computer Science, Engineering or related field and 8+ years of relevant experience, or a Masters degree with 6+ years of experience
Bachelor's degree in Computer Science, Engineering or related field and 12+ years of relevant experience, or a Masters degree with 10+ years of experience
Master’s degree in Computer Science, Engineering or related field and 15+ years of relevant experience, or a Doctorate degree with 13+ years of experience

Nice To Haves

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.
Hands-on 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.
Expertise in designing and implementing safety, guardrails, and bias-mitigation strategies for autonomous agents and multi-agent systems
Experience developing Agentic AI solutions, including autonomous planning–execution–reflection loops, multi-agent collaboration, and coordination at scale
Familiarity with evaluation and observability tools for AI agents, such as LangSmith, OpenAI Evals, or custom telemetry systems
Experience integrating agents with cloud-native workflows, streaming data pipelines, and real-time decision-making environments

Responsibilities

Collaborate with Agentic AI Scientists to build and deploy AI agents to both automate and optimize labor-intensive workflows
Empowering the human workforce to discover entirely new capabilities
Writing software code to support AI agent communication
Connecting models and agents to external services via API calls
Support testing and debugging tasks
Deployment into target environments
Setting up monitoring
Ensuring reliable execution of agentic AI systems
Developing 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
Conduct research, develop prototypes, evaluate and document results, potentially through publication and presentation at conferences and other public forums
Be part of a team developing solutions for deployment into operational environments, or for integration into mission systems
Be a self-starter while also working well within the team, collaborating and sharing discoveries and seeking feedback