{"id":3251618,"url":"https://alion.io/job/campbell-global-lead-software-engineer-ai-engineer","title":"Lead Software Engineer - Ai Engineer","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":95,"open_postings":157,"ghost_share":0.064,"stale_share":0.178,"repost_share":0.121,"time_to_fill_p50_days":13,"computed_at":"2026-11-02T06:13:15Z"}},"role":"AI/ML","role_family":"AI/ML","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"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":123000,"max_usd":254000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":782},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"Claude","optional":false},{"name":"Copilot","optional":false},{"name":"GCP","optional":false},{"name":"Hugging Face","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"PostgreSQL","optional":false},{"name":"PyTorch","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Service Mesh","optional":false},{"name":"TensorFlow","optional":false},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"Helm","optional":true},{"name":"Kubernetes","optional":true},{"name":"Python","optional":true}],"status":"live","first_seen_at":"2026-07-27T18:52:51Z","employer_posted_date":"2026-07-27","last_verified_at":"2026-11-02T15:37:36Z","board_verified":true,"closed_at":null,"days_open":97,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":97},"description":"We have an exciting and rewarding opportunity for you to take your software engineering career to the next level.\nAs a Lead Software Engineer at JPMorganChase within the Commercial & Investment Bank,Automation & AI Solutions team, you will architect, design, and deliver scalable software products that combine cloud-native microservices, and generative AI/LLM capabilities. You will own key technical decisions, drive engineering best practices, and ensure solutions are secure, reliable, and production-ready.\nJob responsibilities:\nDesign and develop creative full-stack software solutions using innovative approaches.\nLead the creation and implementation of AI-driven capabilities, including LLM-based services, orchestration, and integrations into business workflows.\nDrives 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.\n Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.\n Applies 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.\nArchitect and deliver cloud-native microservices and APIs (REST/streaming), ensuring scalability, resilience, and strong security controls.\nIdentify and automate solutions for recurring operational issues to improve system stability and observability (logs/metrics/tracing).\nCommunicate project status clearly and manage priorities across multiple initiatives.\nCollaborate within a Scrum team, participate in Agile ceremonies, and support a culture of diversity, opportunity, and inclusion.\nCodes in Java, AWS ECS, EKS, and Postgres\nUtilizes AI agents (CoPilot, Claude) to improve quality and delivery timelines\nRequired qualifications, capabilities, and skills\nFormal training or certification in Software Engineering and 5+ years applied experience\nStrong system design, application development, and operational stability skills in production environments.\n Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.\n Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.\nInfluence product design, application functionality, and technical operations within the team and domain by proposing pragmatic architectures, tradeoffs, and standards aligned to firm SDLC, security, and controls expectations.\nHands-on experience with Large Language Models (LLMs) and generative AI use cases (e.g., RAG, agents, prompt/tool orchestration, evaluation/guardrails).\nFamiliarity with AI/ML frameworks and ecosystems such as PyTorch, TensorFlow, scikit-learn, Hugging Face.\nExperience with distributed systems and at least one major cloud platform (AWS, GCP, or Azure).\nExpertise in microservices, RESTful APIs, and data technologies (relational and/or NoSQL).\nDemonstrated 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.\nPractical experience building cloud-native systems (event-driven architectures, streaming, service mesh, etc.).\nPreferred qualifications, capabilities, and skills:\nCloud certification in AWS, GCP, or Azure.\nWorking knowledge of Python (for AI/ML integrations) a plus\nExperience with multi region service deployments and zero downtime deployment\n\nFamiliarity with Docker, Kubernetes, Helm, and modern CI/CD practices.\n\nStrong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs\n\nStrong communication skills and a proactive approach to continuous improvement.\n\nTrack record delivering scalable, reliable, and secure products from concept to launch.\n\nAdvanced Java proficiency (primary), plus working knowledge of Python (for AI/ML integrations) a 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