DevOps Architect

EY - GDS Consulting - AIA - Gen AI - Senior Manager

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Job Title

Senior Manager / Architect – GenAI Solutions

 

Role Overview

We are looking for a Senior Manager – GenAI Architect with 14+ years of experience to own the end‑to‑end solution architecture of enterprise GenAI platforms. This role is heavily focused on solution design, PoC & MVP creation, architectural decision‑making, and production-grade implementation, while guiding teams and stakeholders from idea to scalable delivery.

You will act as the technical authority for GenAI, driving architectural excellence, evaluation strategies, and framework selection across enterprise use cases.

 

Key Responsibilities

• Own end‑to‑end GenAI solution architecture, covering ideation, feasibility analysis, PoC execution, MVP design, and production‑ready implementation.
• Lead solution design and architecture workshops, translating complex business problems into scalable and secure GenAI architectures.
• Design, implement, and validate PoCs and MVPs to assess technical feasibility, architectural choices, cost-performance trade‑offs, and enterprise fit.
• Define and standardize enterprise GenAI reference architectures for chatbots, copilots, analytics assistants, and workflow automation use cases.
• Architect advanced RAG pipelines, including chunking strategies, retrieval methods, embedding optimization, reranking, and grounding techniques.
• Drive prompt engineering standards across solutions, including system prompt design, instruction tuning, reasoning control, and consistency guidelines.
• Define and govern prompt evaluation frameworks, covering correctness, faithfulness, hallucination reduction, determinism, latency, cost efficiency, and regression management.
• Lead architectural decisions around Chain‑of‑Thought (CoT), structured reasoning, and explainability strategies.
• Architect and review agentic AI systems using LangChain, LangGraph, and custom orchestration layers, including multi‑agent coordination, tool usage, memory, routing, and fallback patterns.
• Own framework and technology evaluation, selecting appropriate GenAI frameworks based on scalability, reliability, observability, and enterprise constraints.
• Design scalable and secure FastAPI‑based service architectures to expose GenAI capabilities across enterprise platforms.
• Define enterprise security architecture, including JWT‑based authentication, authorization, data access controls, and LLM data protection.
• Lead AWS cloud architecture for GenAI workloads, ensuring scalability, reliability, cost optimization, and secure deployments.
• Architect and guide usage of DynamoDB, defining access patterns, performance optimization, and cost‑efficient designs.
• Define vector database architecture, embedding lifecycle management, similarity tuning, and retrieval performance optimization.
• Own LLM platform and model strategy, including evaluation and usage of AWS Bedrock and Azure OpenAI based on cost, latency, security, compliance, and roadmap alignment.
• Establish LLMOps practices, including prompt versioning, model versioning, evaluation pipelines, deployment strategies, monitoring, rollback mechanisms, and operational governance.
• Design observability and evaluation strategies for GenAI systems, covering logging, tracing, prompt metrics, retrieval metrics, agent behavior, and cost monitoring.
• Define and enforce guardrails and responsible AI practices to minimize hallucinations, prevent data leakage, and ensure compliant and safe GenAI behavior.
• Review and guide engineering teams through architecture reviews, PoC sign‑offs, MVP readiness, and production certification.
• Mentor senior engineers, architects, and managers on GenAI architecture, system design, and enterprise delivery best practices.
• Act as a trusted technical advisor to leadership, providing clarity on feasibility, risks, timelines, and GenAI adoption strategy.

 

Mandatory Skills

• 14+ years of experience in software engineering, solution architecture, or AI platform design
• Strong expertise in Python Object‑Oriented Programming
• Deep hands‑on experience with RAG architectures, chunking strategies, retrieval optimization, CoT, and guardrails
• Advanced Prompt Engineering and prompt evaluation methodologies
• Strong experience with Agentic AI frameworks (LangChain, LangGraph)
• Expertise in FastAPI and API‑led enterprise architecture
• Strong AWS architecture experience, including DynamoDB
• Secure system design using JWT authentication
• Deep understanding of vector databases and embedding optimization
• Hands‑on experience with AWS Bedrock and Azure OpenAI
• Strong knowledge of LLMOps, including evaluation pipelines, deployment, monitoring, versioning, and governance of GenAI systems

 

Good to Have

• Experience with enterprise chatbot, copilot, or analytics‑driven GenAI platforms
• Exposure to compliance‑heavy or regulated enterprise environments
• Thought leadership in GenAI architecture, governance, or AI platform strategy

 

What we are looking for

• Strong architectural ownership with a builder’s mindset
• Proven ability to convert ambiguous ideas into working PoCs and MVPs
• Deep understanding of GenAI reliability, evaluation, and operationalization (LLMOps)
• Ability to balance innovation, cost, performance, and enterprise risk
• Strong stakeholder communication and technical influence
• Vision to scale GenAI solutions from experimentation to enterprise platforms

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Experience Level

Senior Level