{"id":1888445,"url":"https://alion.io/job/ernst-young-engineering-lead-ai","title":"Engineering Lead - AI","company":{"id":2859,"name":"EY","domain":"ey.com","url":"https://alion.io/company/ey-com","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"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":["Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":26000,"max_usd":63000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":31},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"C#","optional":false},{"name":"CI/CD","optional":false},{"name":"Embeddings","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"Java","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PoC Library","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Azure","optional":true},{"name":"Power Apps","optional":true},{"name":"Power Automate","optional":true},{"name":"ServiceNow","optional":true}],"status":"live","first_seen_at":"2026-10-05T02:00:00Z","employer_posted_date":"2026-10-05","last_verified_at":"2026-10-13T09:39:15Z","board_verified":true,"closed_at":null,"days_open":8,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":8},"description":"At EY, we’re all in to shape your future with confidence.\nWe’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go.\nJoin EY and help to build a better working world.\nEY Job Description\nJob Title: Engineering Lead - AI\nJob Rank (must note a single rank only for each job description): Associate Director\nFunction: EY Technology - Enterprise Technology\nScope (indicate either “global/cross-border” or “local/country”):Global\nSub Function: EY Technology | CBS Technology | Intelligent Automation\nReports to (Job Title): Service Delivery Lead: AI & Automation\nJob Summary:\nThe Engineering Lead is responsible for hands-on engineering delivery of enterprise AI/ML/GenAI and automation solutions, coding standards, design patterns, ensuring production-grade security, scalability, reliability, and supportability. It is not a solution advisory, PoC, or platform administration role. Success is measured by the quality, reliability, scalability, and operability of AI systems in production.\nOperating as a player-coach, the Engineering Lead works alongside AI Engineers to design and build ML Models, LLM pipelines and agentic workflows, while also setting engineering standards, coaching teams, and ensuring delivery.\nThe ideal candidate will possess:\nExtensive, hands-on software engineering experience, with a proven track record of building and operating complex systems in production environments.\nStrong executive communication and stakeholder management skills, with the ability to translate complex technical concepts into clear, business-relevant outcomes.\nAbility to independently design and code across multiple technical components, while guiding and elevating the work of senior engineers.\nDeep technical expertise across the full stack, including cloud-native architectures, distributed systems, and data-intensive platforms.\nStrong command of non-functional requirements, including reliability, availability, scalability, performance, and cost optimization, and the ability to make sound technology and architecture decisions as systems evolve over time.\nDemonstrated experience delivering production-grade Generative AI and Agentic AI solutions, including:\nLLM-powered applications and services\nAgentic workflows and orchestration frameworks\nModel integration, evaluation, and lifecycle management\nMLOps / LLMOps pipelines and operational practices\nProven experience partnering with Data Science and AI Research teams to operationalize models and AI capabilities at enterprise scale.\nAbility to drive the design and delivery of AI-first architectures, including LLM-powered services, agentic workflows, orchestration layers, and human-in-the-loop systems.\nExperience building robust data and software foundations that enable advanced analytics, real-time AI inference, and intelligent decisioning at scale.\nEssential Functions of the Job:\nEngineering Ownership & Delivery AccountabilityOwn end-to-end technical delivery of AI and GenAI solutions-from design through production and BAU support.\nAct as the technical authority for the build team, accountable for:Code quality and engineering standards\nSecurity, privacy, and compliance\nReliability, scalability, performance, and cost\nOperational readiness and supportability\n\nMake hard engineering trade-offs balancing latency, accuracy, cost, reliability, and scale.\nOwn production systems post go-live, including incident analysis, performance tuning, and architectural evolution.\n\nHands-on GenAI & AI Systems Engineering\nCollaborate with solution architecture on design and own build production-grade GenAI systems, including:Retrieval-Augmented Generation (RAG) pipelines\nAgentic workflows and tool-based orchestration\nPrompt pipelines, routing, and integration layers\nHuman-in-the-loop and safety guardrails\n\nWork shoulder-to-shoulder with AI Engineers and Data Scientists to:Productionize models and LLM pipelines\nImplement evaluation, monitoring, and observability\nOptimize inference cost, performance, and reliability\n\nEnsure experimental AI capabilities are engineered into real systems, not isolated prototypes.\n\nArchitecture & Engineering StandardsDefine and enforce reference architectures and engineering standards for AI and GenAI systems.\nDrive consistent adoption of:Clean and modular architecture patterns\nReusable components and shared frameworks\nCI/CD, DevOps, and cloud-native practices\n\nPartner with architecture, platform, security, and data teams while retaining final accountability for build quality.\n\nAI Platform Engineering & MLOps / LLMOpsBuild and evolve AI platforms that support:Model lifecycle management\nLLMOps / MLOps pipelines\nEvaluation, monitoring, and drift detection\nSecure access, auditability, and governance\n\nEnsure AI systems meet enterprise non-functional requirements over time, not just at launch.\n\nTeam Leadership & Capability BuildingLead and grow a team of senior engineers within the AI & Automation build function.\nCoach engineers on:Backend and distributed systems engineering\nGenAI application design and implementation\nWriting maintainable, testable, production-quality code\n\nSet a strong engineering culture focused on ownership, rigor, and continuous improvement.\nSupport hiring and skills development aligned to future AI engineering needs.\n\nStakeholder & Cross-Functional CollaborationPartner closely with:AI/ML Engineering and Data Science teams\nPlatform, security, and operations teams\nArchitecture and governance forums\n\nTranslate business and functional requirements into robust technical designs.\nAct as a senior technical voice in design authorities and delivery governance.\n\nEngineering Expectations (Non Negotiable)\nHas built and operatedcomplex, distributed backend systems in production.\nComfortable debuggingproduction incidents involving AI/LLM pipelines.\nWrites, reviews, and maintains production-quality code, not just prototypes.\nUnderstands failure modes of AI systems and designs for resilience.\nTakes ownership for systemsafter go-live, including reliability and cost optimization.\nAnalytical/Decision Making Responsibilities:\nThis role is critical to ensuring the enterprise’s AI ambition translates into real, reliable, and scalable systems -not just innovation theater. You will define how AI is built, shipped, and operated across the organization.\nKnowledge and Skills Requirements:\nCore Engineering Skills\nStrong hands-on experience in Python, Java, C#, or similar backend languages.\nProven experience building API-driven, cloud-native systems.\nStrong understanding of distributed systems, scalability, and reliability patterns.\nGenAI / AI Engineering (Essential)\nHands-on experience building LLM-powered applications, including:RAG pipelines\nAgentic or tool-augmented workflows\nPrompt orchestration and integration layers\n\nExperience with:Vector databases and embeddings\nLLM evaluation, observability, and monitoring\nGuardrails, safety, and cost controls\n\nExperience operationalizing AI models at scale in enterprise environments.\nNice to Have (Not Core Identity)\nExperience integrating AI services into enterprise automation platforms (e.g., Power Platform, ServiceNow).\nFamiliarity with Azure AI services and data platforms.\nDetailed Responsibilities:\n Solution Development: Design, develop, and implement automation solutions using Microsoft Power Platform (Power Automate, Power Apps) and ServiceNow. Create custom workflows, forms, and applications to automate business processes and enhance user experience.\nIntegration Management: Integrate Microsoft and ServiceNow platforms with other enterprise applications using APIs, web services, and middleware. Ensure seamless data flow between systems and maintain data integrity across integrated applications.\nTesting and Quality Assurance: Conduct thorough testing of automation solutions to ensure functionality, performance, and compliance with business requirements. Implement best practices for code quality, documentation, and version control.\nObservability and Maintenance: Monitor the performance of automation solutions and troubleshoot issues as they arise. Perform regular maintenance and updates to ensure optimal functionality and security of automation applications.\nDocumentation: Create and maintain comprehensive documentation for automation solutions, including design specifications, user guides, and technical manuals. Document processes, workflows, and integration points for future reference and knowledge transfer.\nUser Training and Support: Provide training and support to end-users and team members on automation tools and processes.Develop training materials and conduct workshops to facilitate user adoption of automation solutions.\nCollaboration and Stakeholder Engagement: Work closely with cross-functional teams, including business analysts, project managers, and IT staff, to ensure alignment on automation initiatives. Engage with stakeholders to gather feedback and make necessary adjustments to automation solutions.\n Performance Metrics and Reporting: Define and track key performance indicators (KPIs) to measure the effectiveness of automation solutions. Generate reports and dashboards to communicate the impact of automation on business processes and operational efficiency.\nContinuous Improvement: Stay updated on the latest trends and advancements in automation, AI, and relevant technologies.Identify areas for continuous improvement and propose enhancements to existing automation solutions.\nGovernance and Compliance: Ensure that automation solutions adhere to organizational policies, governance frameworks, and compliance requirements. Participate in risk assessments and audits related to automation initiatives.\nInnovation and Research: Explore and evaluate new technologies, tools, and methodologies to enhance automation capabilities. Conduct research on industry best practices and competitor strategies to inform decision-making and innovation.\nChange Management: Analyze the impact of automation on organizational processes and culture, making recommendations for change management strategies. Assist in developing communication plans to inform stakeholders about automation initiatives and their benefits.\nOther Requirements:\nFamiliarity with Python practices and tools.\nStrong communication, stakeholder management and influence\nStrong leadership and conflict resolution capability\nLeading virtual teams\nJob Requirements:\nEducation:\nA degree in Computer Science / Engineering or a related discipline; or equivalent work experience\nExperience:\n10+ years in a Global IT environment working with multiple disciplines to deliver projects in line with customer needs\n3+ Years Global delivery or transformation preferably in large scale infrastructure programs\n3+ Years in a global operations environment\nCertification Requirements:\nRelevant certifications in MS Power Platform and ServiceNow RPA\nPython Certification\nAzure AI Services\nAzure Data Services\nEY | Building a better working world\nEY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets.\nEnabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow.\nEY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. 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