{"id":3955453,"url":"https://alion.io/job/gspann-ai-engineer-enterprise-platform-engineering-2","title":"AI Engineer - Enterprise Platform Engineering","company":{"id":2977679,"name":"GSPANN","domain":"gspann.com","url":"https://alion.io/company/gspann","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Hyderabad, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":6,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ChatGPT","optional":false},{"name":"CI/CD","optional":false},{"name":"Machine Learning","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenAI Codex","optional":false},{"name":"Python","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Agile","optional":true},{"name":"Confluence","optional":true},{"name":"GitHub","optional":true},{"name":"GitLab","optional":true},{"name":"Jira","optional":true},{"name":"Kubernetes","optional":true},{"name":"LLM","optional":true}],"status":"live","first_seen_at":"2026-11-09T07:35:20Z","employer_posted_date":null,"last_verified_at":"2026-11-09T07:35:20Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"About this team:\n\nThe AI Engineering team builds secure, reliable, and observable AI-enabled systems that help engineering teams understand code, automate repetitive work, and make better decisions. You will work with developers, security teams, researchers, and platform partners to turn emerging AI capabilities into production-grade tools that improve software quality across repositories, languages, and teams.\n\nWhat you'll do:\nDesign, build, and maintain an AI-driven code review system that helps agents understand code changes, investigate repository context, and produce actionable findings.\nDevelop agent execution loops that coordinate model responses, code search, static analysis, and test execution to identify bugs, security vulnerabilities, and engineering standards violations.\nBuild secure execution environments and workflow infrastructure for reviews, including sandboxing, access controls, state management, retries, and integration with pull requests and CI/CD pipelines.\nCreate evaluation datasets and debugging tools that measure review quality, detect regressions, and distinguish model errors from context retrieval, tool execution, and orchestration failures.\nRun controlled experiments across models, prompts, context selection, and review strategies to improve issue detection, reduce false positives, and manage latency and cost.\nOwn production reliability and observability, including review traces, model usage, failure diagnostics, and incident response.\nWork with developers, security teams, and researchers to turn engineering standards and reviewer feedback into measurable improvements in review accuracy and usefulness.\nBuild shared tools and integrations that make automated reviews easier to extend and maintain across repositories, languages, and teams.\n\nQualifications:\nBachelor's degree or diploma in Computer Science, Software Engineering, Machine Learning, or a related field preferred; equivalent relevant industry experience is also accepted.\n6-8 years of software engineering experience, including hands-on work with AI-enabled or developer-tooling systems.\n\nImportant skills:\nComfortable using emerging technologies such as Codex, ChatGPT, and the OpenAI API to script or engineer Python solutions that automate repetitive tasks and integrate tools.\nExpert-level SaaS troubleshooting skills, with the ability to rapidly understand new technologies and complex concepts.\nStrong Python engineering skills and experience building production services, APIs, and automation.\nExperience integrating large language models through APIs and designing prompts, tool calls, context selection, and structured outputs.\nExperience with code search, repository analysis, static analysis, test automation, or developer productivity platforms.\nUnderstanding of agent orchestration, retrieval workflows, state management, retries, and failure recovery.\nExperience building evaluation datasets, quality metrics, regression tests, and debugging tools for AI systems.\nKnowledge of secure software design, sandboxing, access controls, secrets handling, and vulnerability detection.\nStrong observability and production troubleshooting skills across traces, logs, metrics, model usage, and distributed workflows.\nStrong written and verbal communication skills, with experience working across engineering, security, product, and research teams.\n\nGood to have skills:\nDevOps experience, including CI/CD, infrastructure as code, release automation, and operational ownership.\nExperience with containers, Kubernetes, cloud platforms, and secure ephemeral execution environments.\nFamiliarity with GitHub or GitLab pull-request workflows, webhooks, and CI integrations.\nExperience with SRE practices, incident response, reliability targets, and cost management.\nWorking knowledge of Agile delivery and collaboration tools such as Jira and Confluence.\nSkills\nAgent Orchestration, ChatGPT, Code Search, Codex, LLM, Large Language Models, Observability, OpenAI API, Python, Sandbox, Static 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