Engineering Lead - AI
Ernst Young · Kochi · onsite · Posted 43d ago
Undisclosed5–10 yrsfulltimeSourced from
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About this role
Job Title: Engineering Lead AI
Job Rank: Associate Director
Function: EY Technology Enterprise Technology
Scope: Global
Sub Function: EY Technology | CBS Technology | Intelligent Automation
Reports to (Job Title): Service Delivery Lead: AI & Automation
Job Summary The 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.
Operating as a playercoach, 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.
The ideal candidate will possess: - Extensive, hands-on software engineering experience, with a proven track record of building and operating complex systems in production environments.
- Strong executive communication and stakeholder management skills, with the ability to translate complex technical concepts into clear, business-relevant outcomes.
- Ability to independently design and code across multiple technical components, while guiding and elevating the work of senior engineers.
- Deep technical expertise across the full stack, including cloud-native architectures, distributed systems, and data-intensive platforms.
- Strong 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.
- Demonstrated experience delivering production-grade Generative AI and Agentic AI solutions, including:
- LLM-powered applications and services
- Agentic workflows and orchestration frameworks
- Model integration, evaluation, and lifecycle management
- MLOps / LLMOps pipelines and operational practices
- Proven experience partnering with Data Science and AI Research teams to operationalize models and AI capabilities at enterprise scale.
- Ability to drive the design and delivery of AI-first architectures, including LLM-powered services, agentic workflows, orchestration layers, and human-in-the-loop systems.
- Experience building robust data and software foundations that enable advanced analytics, real-time AI inference, and intelligent decisioning at scale.
- Engineering Ownership & Delivery Accountability
- Own end-to-end technical delivery of AI and GenAI solutionsfrom design through production and BAU support.
- Act as the technical authority for the build team, accountable for:
- Code quality and engineering standards
- Security, privacy, and compliance
- Reliability, scalability, performance, and cost
- Operational readiness and supportability
- Make hard engineering trade-offs balancing latency, accuracy, cost, reliability, and scale.
- Own production systems post go-live, including incident analysis, performance tuning, and architectural evolution.
- Hands-on GenAI & AI Systems Engineering
- Collaborate with solution architecture on design and own build production-grade GenAI systems, including:
- Retrieval-Augmented Generation (RAG) pipelines
- Agentic workflows and tool-based orchestration
- Prompt pipelines, routing, and integration layers
- Human-in-the-loop and safety guardrails
- Work shoulder-to-shoulder with AI Engineers and Data Scientists to:
- Productionize models and LLM pipelines
- Implement evaluation, monitoring, and observability
- Optimize inference cost, performance, and reliability
- Ensure experimental AI capabilities are engineered into real systems, not isolated prototypes.
- Collaborate with solution architecture on design and own build production-grade GenAI systems, including:
- Architecture & Engineering Standards
- Define and enforce reference architectures and engineering standards for AI and GenAI systems.
- Drive consistent adoption of:
- Clean and modular architecture patterns
- Reusable components and shared frameworks
- CI/CD, DevOps, and cloud-native practices
- Partner with architecture, platform, security, and data teams while retaining final accountability for build quality.
- AI Platform Engineering & MLOps / LLMOps
- Build and evolve AI platforms that support:
- Model lifecycle management
- LLMOps / MLOps pipelines
- Evaluation, monitoring, and drift detection
- Secure access, auditability, and governance
- Ensure AI systems meet enterprise non-functional requirements over time, not just at launch.
- Build and evolve AI platforms that support:
- Team Leadership & Capability Building
- Lead and grow a team of senior engineers within the AI & Automation build function.
Sourced from naukri
Skills
AutomationAutomation ToolsApplication DesignAzure Aiapisai servicesai modelsauditsAi ResearchAnalyticsArchitectureAdvanced AnalyticsAdministrationAi PlatformAi SolutionsMicrosoft AzureArtificial IntelligenceAutomation Solutions
About Ernst & Young
Ernst Young
Management Consulting
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India
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