{"id":3016702,"url":"https://alion.io/job/american-airlines-sr-engineer-it-machine-learning","title":"Sr. Engineer - IT Machine Learning","company":{"id":12144,"name":"American Airlines","domain":"americanairlines.in","url":"https://alion.io/company/american-airlines","size_band":null,"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, 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implementations of significant capabilities in existing systems.\nCoaches team members in the execution of techniques to improve reliability, resiliency, security, and performance.\nDecomposes intricate and interconnected designs into implementations that can be effectively built and maintained by less experienced engineers.\nAnticipates trouble areas in systems under development and guides the team in instrumentation practices to ensure observability and supportability.\nDefines test suites and instrumentation that ensures targets for latency and availability are being consistently met in production.\nLeads through example by prioritising the closure of open vulnerabilities.\nEvaluates potential attack surfaces in systems under development, identifies best practices to mitigate, and guides teams in their implementation.\nLeads team in the identification of small batches of work to delivery the highest value quickly\nEnsures reuse is a first-class consideration in all team implementations and is a passionate advocate for broad reusability\nFormally mentors teammates and helps guide them to and along needed learning journeys.\nObserves their environment and identifies opportunities for introducing new approaches to problems.\n\nRequirements: \nBachelor's degree in Computer Science, Computer Engineering, Technology, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training\n3+ years of experience designing, developing, and implementing large-scale solutions in production environments.\nMaster's degree in Computer Science, Computer Engineering, Technology, Information Systems (CIS/MIS), Engineering or related technical discipline, or equivalent experience/training\nAirline Industry experience.\nAbility to effectively communicate both verbally and in writing with all levels within the organisation.\n\nProficiency and demonstrated experience in the following technologies/frameworks:\n\nProgramming and Backend Development: \nPython (backend services/APIs, automation, testing).\nSQL (advanced querying, window functions, optimisation, data modelling fundamentals).\nREST APIs, JSON, service integration patterns.\n\nAI Agents / Agent Frameworks (Must-have): \nHands-on experience with at least one enterprise-grade agent framework such as:\nLangGraph/Crew AI/ Autogen (or equivalent agent orchestration frameworks).\nCopilot Studio agents /Open AI agents (or equivalent).\nAgent orchestration, tool/function calling, structured outputs, evaluation and iteration patterns.\n\nAnalytics, Reporting and Dashboards: \nPower BI and/or Tableau.\nMetrics definition, reporting automation, executive-ready dashboards.\n\nCloud Platforms: \nMicrosoft Azure or AWS.\nExperience deploying data/AI workloads in cloud environments.\n\nData and Integration: \nData pipelines/ELT concepts, dataset curation for analytics.\nIntegration with an enterprise risk assessment and governance workflow platform (tool-agnostic).\n\nAI/ML Fundamentals: \nApplied understanding of ML lifecycle concepts\nFamiliarity with LLM concepts like prompting, context handling, grounding patterns like RAG, Evals, Agent Behaviour Monitoring, etc.\n\nGovernance, Risk and Regulatory Knowledge: \nWorking awareness of AI/data regulations and governance expectations such as GDPR, EU AI Act, and similar privacy/AI governance frameworks.\nUnderstanding of governance controls: evidence capture, auditability, traceability, Observability, exception management, and reporting.\n\nSoftware Engineering Practices: \nObject-oriented design and clean coding principles.\nVersion control (Git) and CI/CD fundamentals.\nTesting practices (unit/integration), documentation, logging/monitoring, operational readiness.\n\nLicenses and Certifications (Preferred, not required): \nMicrosoft Certified: Azure Data Engineer Associate / Azure Developer Associate / Azure AI Engineer Associate.\nAWS Certified Developer - Associate / AWS 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