Consulting - Senior Manager, AI Agentic Platform Engineering (Backend / Python)

Job Summary

This hiring is for the EY.ai Center for Reimagination, EY s Client Zero environment for demonstrating how enterprise AI can move from experimentation to governed, production-scale transformation. The Center brings together AI-native engineering, immersive experiences, digital humans, robotics, IoT, cloud, edge, backend platforms and DevSecOps into one unified, production-grade technology fabric. The role will contribute to building reusable AI and agentic engineering patterns that support industry experiences across sectors such as Life Sciences, Banking and Capital Markets, Consumer Products and Retail, Industrial Products and Energy. It is suited for a leader who wants to shape platform-first AI delivery, industrialise agentic solutions and help scale EY s next generation of client-facing AI capabilities.

The Opportunity

We are looking for a Senior Manager for the EY.ai Center for Reimagination who can lead backend and AI agentic platform engineering across complex enterprise transformation programmes. The role will own technical direction, delivery governance and capability scaling for Python-led backend platforms and production-grade agentic solutions, leveraging frameworks such as Microsoft Agent Framework, LangGraph, OpenAI Agents SDK, Semantic Kernel, AutoGen, CrewAI or equivalent orchestration patterns to deliver secure, scalable and commercial client outcomes. The successful candidate will operate at the intersection of architecture, programme delivery, people leadership and stakeholder management. This role requires the ability to shape platform strategy, lead managers and senior engineers, influence client and internal leadership, and establish reusable engineering practices that can be scaled across programmes.

Your Key Responsibilities

- Own the backend platform engineering strategy across multiple workstreams, teams or programmes, with transparent alignment to business objectives and client outcomes.




- Define and govern architecture principles for Python-led backend platforms, Go services, API ecosystems, integration layers, data movement and cloud-native deployment models.
- Lead technical governance forums, architecture reviews, release readiness checkpoints, dependency management and risk review mechanisms.
- Partner with senior stakeholders, programme leaders, product owners and architects to convert strategic priorities into executable engineering roadmaps.
- Build and scale high-performing engineering teams by defining role clarity, capability needs, staffing plans, mentoring models and quality expectations.
- Drive consistent engineering standards across secure coding, automated testing, CI/CD, observability, documentation, operational resilience and production support readiness.
- Identify platform investment areas, reusable assets, accelerators and automation opportunities that improve delivery velocity and reduce programme risk.
- Manage technical escalations, unblock cross-team dependencies and ensure complex delivery issues are resolved with clear ownership and decision-making.
- Guide managers, architects and senior developers through design trade-offs, delivery planning, quality gates and stakeholder communication.
- Promote AI-assisted engineering, agentic application architecture and agent-led delivery practices where they improve productivity, code quality, testing coverage, engineering knowledge reuse and enterprise automation outcomes.
- Define reference architectures for agentic applications using frameworks such as Microsoft Agent Framework, LangGraph, OpenAI Agents SDK, Semantic Kernel, AutoGen, CrewAI or similar orchestration patterns, including tool calling, memory, guardrails,



human-in-the-loop workflows, observability and enterprise integration.
- Lead the design and delivery of agentic workflow patterns that combine reasoning, planning, tool execution, retrieval, memory and business-rule orchestration for enterprise-grade use cases.
- Establish engineering guardrails for responsible AI, model selection, prompt patterns, evaluation metrics, hallucination controls, auditability, data privacy and secure enterprise integration.
- Drive reusable agentic accelerators, templates and platform services that improve developer productivity, standardise delivery and enable repeatable client solutions across sectors.
- Collaborate with cloud, data, security, UX and domain teams to industrialise AI agents into production-ready platforms with clear operating models, monitoring, fallback paths and human oversight.

Skills and attributes for success

Must-have technical and leadership skills

- Strong background in backend engineering leadership with proven experience across Python, Java and polyglot microservices ecosystems.
- Ability to lead architecture strategy for secure, scalable, observable and cloud-ready enterprise platforms.
- Experience managing multiple engineering workstreams, senior engineers, technical architects or delivery pods within complex programmes.
- Deep understanding of API design, microservices, database integration, DevOps, CI/CD, automated quality gates and production-readiness practices.
- Robust delivery governance capability, including estimation, risk management, release governance, dependency tracking and stakeholder reporting.
- Commercial and business-outcome orientation, with the ability to connect engineering decisions to client value, cost, quality, productivity and risk reduction.

Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Tech S and T-Solution Architect-SM-G (Bengaluru)
🏢 EY
📍 Bengaluru