Engineering Lead - AI

Engineering Lead - AI

Location: Kochi Other locations: Anywhere in Country Salary: Competitive Date: 24 Apr 2026

Job Description

Requisition ID: 1701515 At EY, we're all in to shape your future with confidence. We'll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go. Join EY and help to build a better working world.

EY Job Description Job Title: Engineering Lead – AI Job Rank (must note a single rank only for each job description): Associate Director Function: EY Technology – Enterprise Technology Scope (indicate either "global/cross-border" or "local/country"):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 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. 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.

Essential Functions of the Job:

• Engineering Ownership & Delivery Accountability Own end-to-end technical delivery of AI and GenAI solutions—from 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.
• 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.
• Team Leadership & Capability Building Lead and grow a team of senior engineers within the AI & Automation build function. Coach engineers on: Backend and distributed systems engineering GenAI application design and implementation Writing maintainable, testable, production-quality code Set a strong engineering culture focused on ownership, rigor, and continuous improvement. Support hiring and skills development aligned to future AI engineering needs.
• Stakeholder & Cross-Functional Collaboration Partner closely with: AI/ML Engineering and Data Science teams Platform, security, and operations teams Architecture and governance forums Translate business and functional requirements into robust technical designs. Act as a senior technical voice in design authorities and delivery governance.

Engineering Expectations (Non Negotiable) Has built and operated complex, distributed backend systems in production. Comfortable debugging production incidents involving AI/LLM pipelines. Writes, reviews, and maintains production-quality code, not just prototypes. Understands failure modes of AI systems and designs for resilience. Takes ownership for systems after go-live, including reliability and cost optimization.

Analytical/Decision Making Responsibilities: This 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.

Knowledge and Skills Requirements: Core Engineering Skills Strong hands-on experience in Python, Java, C#, or similar backend languages. Proven experience building API-driven, cloud-native systems. Strong understanding of distributed systems, scalability, and reliability patterns.

GenAI / AI Engineering (Essential) Hands-on experience building LLM-powered applications, including: RAG pipelines Agentic or tool-augmented workflows Prompt orchestration and integration layers Experience with: Vector databases and embeddings LLM evaluation, observability, and monitoring Guardrails, safety, and cost controls Experience operationalizing AI models at scale in enterprise environments.

Nice to Have (Not Core Identity) Experience integrating AI services into enterprise automation platforms (e.g., Power Platform, ServiceNow). Familiarity with Azure AI services and data platforms.

Detailed Responsibilities: 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. Integration 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. Testing 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. Observability 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. Documentation: 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. User 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. Collaboration 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. 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. Continuous 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. Governance 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. Innovation 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. Change 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. Other Requirements: Familiarity with Python practices and tools. Strong communication, stakeholder management and influence Strong leadership and conflict resolution capability Leading virtual teams

Job Requirements: Education: A degree in Computer Science / Engineering or a related discipline; or equivalent work experience Experience: 10+ years in a Global IT environment working with multiple disciplines to deliver projects in line with customer needs 3+ Years Global delivery or transformation preferably in large scale infrastructure