Senior Consulting Manager, AI Architecture & Engineering

Cognizant

Confirmed live today High trust

Hybrid

Quick summary

Work type: Hybrid
Location: Atlanta, GA
Salary: $98,853–$182,500 / yr
Posted: 26 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $201k

This role $141k

$79k $282k

below market most similar roles pay here above market

This role pays less than 91% of similar roles. Most pay $167,125–$235,750 — the blue band above. At the midpoint, this role pays about $141k versus about $201k for comparable roles.

Based on 240 similar postings.

Employer

About Cognizant

Cognizant is a multinational IT services and consulting company offering digital transformation, technology, and business process services. It serves clients across financial services, healthcare, manufacturing, and other industries worldwide.

Cognizant currently has 330 open roles on FindRole.

Listed pay typically runs $100,000–$135,000 across 211 roles with salary data.

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View all roles at Cognizant

At a glance

TL;DR · Senior Consulting Manager, AI Architecture & Engineering

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The Senior Consulting Manager - AI Architecture & Engineering joins the consulting team to translate business priorities into secure, scalable, production-grade AI platforms and customer-facing solutions. You will define target architectures, reference patterns, and engineering standards for enterprise AI, generative AI, retrieval-augmented generation, and agentic applications. Daily responsibilities include leading solutions from discovery through production operations, performing design reviews, and establishing LLMOps practices like automated evaluation, CI/CD, and observability. You will architect model-agnostic solutions using frontier models, multi-agent workflows, and vector or graph retrieval. Required technical skills include Angular, TypeScript, C#, .NET, PostgreSQL, Redis, and Google Cloud services like Vertex AI and Gemini. You must possess expertise in distributed systems, cloud architecture, and establishing responsible-AI controls within complex enterprise environments to deliver measurable operational outcomes.

Skills

Generative AI LLMOps Retrieval-Augmented Generation Vertex AI Gemini LangChain LlamaIndex LangGraph Semantic Kernel TypeScript C# .NET Angular PostgreSQL AlloyDB Redis GKE Cloud Run Pub/Sub CI/CD GraphRAG Distributed Systems

What you'll do

Define and govern target architectures, reference patterns, and engineering standards for enterprise and generative AI.
Lead solutions from initial discovery and architecture through implementation, deployment, and production operations.
Architect model-agnostic AI solutions using frontier models, multi-agent workflows, and vector or graph retrieval.
Establish LLMOps practices including prompt versioning, automated evaluation, CI/CD, observability, and cost optimization.
Mentor and develop architects and engineers while managing technical tradeoffs and vendor assessments.
Translate business priorities into secure, scalable, production-grade AI platforms and customer-facing solutions.
Implement responsible-AI controls, security integrations, and incident response protocols for AI-enabled products.

What we're looking for

12+ years of software, platform, data, or cloud engineering experience.
5+ years of experience in architecture or technical leadership with enterprise-scale production AI systems.
Hands-on expertise in LLM application engineering, agents, RAG, evaluation, guardrails, distributed systems, and cloud architecture.
Experience establishing secure AI platform and LLMOps practices including CI/CD, observability, incident response, and cost optimization.
Experience with full-stack engineering using Angular, TypeScript, C#/.NET, PostgreSQL, Redis, and Google Cloud services like Vertex AI and GKE.
Strong executive communication, client-facing leadership, cross-functional collaboration, and technical decision-making skills.
Experience with LangGraph, Semantic Kernel, LangChain, LlamaIndex, GraphRAG, or comparable frameworks (preferred).
Experience applying AI-native and specification-driven engineering practices including coding agents and automated testing (preferred).