{"id":3145652,"url":"https://alion.io/job/campbell-global-senior-lead-software-engineer-agentic-aijavapython","title":"Senior Lead Software Engineer - Agentic AI/Java/Python","company":{"id":2649833,"name":"Campbell Global","domain":"campbellglobal.com","url":"https://alion.io/company/campbellglobal","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":100,"open_postings":77,"ghost_share":0.013,"stale_share":0.013,"repost_share":0.078,"time_to_fill_p50_days":9,"computed_at":"2026-10-25T05:49:26Z"}},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Plano, United States","Columbus, United 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Digital Technology, you will design and build critical technology solutions across Chase AI components, including Chase Agent, Agent Operating Memory, Domain Agents, Agentic Experience Service, and Assurance.\nJob responsibilities:\nExecutes software solutions, design, development, and technical troubleshooting, thinking beyond routine approaches to solve complex problems.\nCreates secure, high-quality production code and maintain algorithms that run synchronously with appropriate systems.\nProduces architecture and design artifacts for complex applications, ensuring design constraints are met by software code development.\nGathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets to drive continuous improvement of software applications and systems.\nProactively identifies hidden problems and patterns in data, using insights to drive improvements in coding hygiene and system architecture.\nContributes to software engineering communities of practice and events that explore new and emerging technologies.\nAdopts and learn new technologies that positively impact agentic solutions.\nCollaborates with cross-functional teams, including product, design, and operations, to deliver end-to-end solutions.\nEnsures compliance with security, privacy, and regulatory requirements in all software solutions.\n Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team\nApplies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation\nRequired qualifications, capabilities, and skills:\nFormal training or certification on software engineering concepts and 5+ years applied experience with 2+ years building complex scalable applications or agentic systems.\nHands-on experience building agentic systems using LLMs/SLMs\nExperience setting up and maintaining MCP servers and building MCP-compatible tools/adapters \nProficient in coding in one or more languages: Java and/or Python\nProficiency building production services with either Spring AI or the Spring ecosystem (Spring Boot, Spring Security, Spring Cloud), or Python (FastAPI/Flask), with typed contracts, testing, and packaging.\nSolid AWS background with working knowledge of ECS or EKS, containerization (Docker), and CI/CD (GitHub Actions/Jenkins/CodeBuild).\nStrong API design skills (REST/OpenAPI; gRPC and familiarity with observability stacks (e.g., Splunk, CloudWatch, Prometheus/Grafana, OpenTelemetry).\nPractical understanding of LLM patterns: function calling/tools, RAG, prompt management, context windows, token budgeting, and safety guardrails.\nStrong testing culture: unit/integration tests, load tests, and evaluation datasets for agents.\n Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security\nStrong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices\nPreferred qualifications, capabilities, and skills:\nExpertise with distributed orchestration patterns for LLM applications (graph-based flows, retries, fallbacks, guardrails) and secure integration with enterprise tools and 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