Key skills
Agentic AI solutionsGenerative AI modelsLarge Language ModelsPythonAPI integrationNatural Language ProcessingVector databasesSoftware Development LifecycleBashPowerShellAIMLNLPGenerative AIOpenAIRAGLangChainAgenticAutoGenPyTorchTensorFlowLangGraphPineconeWeaviateAzureGCPKubernetesDockerBedrockVertex AISDLCCommunicationCollaboration
About this role
Leidos is an industry and technology leader serving government and commercial customers with smarter, more efficient digital and mission innovations. The AI Engineer will contribute to AI solutions across various domains, focusing on developing and deploying Trusted Mission AI systems that are ethical, transparent, and reliable. Responsibilities include collaborating with teams, creating software for AI agents, and ensuring adherence to ethical guidelines while optimizing workflows.
Responsibilities:
- Collaborate with Agentic AI Scientists to build and deploy AI agents to automate and optimize labor intensive workflows
- Empower the human workforce to discover entirely new capabilities
- Create software to support AI agent communication
- Connect models and agents to external services via API calls
- Test and debug 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 evaluating 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
Requirements:
- Bachelor's degree in Computer Science, Engineering or related field and relevant experience
- Bachelor's degree in Computer Science, Engineering or related field and 2+ years of relevant experience, or a Masters degree with relevant experience
- Bachelor's degree in Computer Science, Engineering or related field and 4+ years of directly applicable experience, or a Masters degree and 2+ years of directly applicable experience
- 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)
- Ability to obtain a Secret clearance
- 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