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About This Role
About Cognizant Consulting
Cognizant Consulting is more than Cognizant's consulting practice - we're a global community of 5,000+ experts dedicated to helping clients reimagine their business. Blending our deep industry and technology advisory capability, we create innovative business solutions for Fortune 500 clients. And now, we're looking for our next colleague who'll join us in shaping the future of business. Could it be you?
About the role
As an Associate Director, AI Architecture and Engineering Lead, you will make an impact by translating business priorities into secure, scalable, production-grade AI platforms and customer-facing solutions. You will be a valued member of the consulting team and work collaboratively with product, data, cloud, security, risk, legal, business leaders, engineering teams, and clients.
In this role, you will:
- Define and govern target architectures, reference patterns, reusable services, and engineering standards for enterprise AI, generative AI, retrieval-augmented generation, agentic applications, and AI-enabled digital products.
- Lead solutions from discovery and architecture through implementation, evaluation, deployment, and production operations, while remaining hands-on in design reviews, prototyping, troubleshooting, and critical technical decisions.
- Architect model-agnostic AI solutions using frontier and open models, stateful agents, multi-agent workflows, tool calling, memory, human approvals, Model Context Protocol, vector or graph retrieval, and secure enterprise integrations.
- Establish AI platform and LLMOps practices covering prompt and model versioning, automated evaluation, CI/CD, observability, rollback, incident response, latency, reliability, cost optimization, and responsible-AI controls.
- Lead, mentor, and develop architects and engineers while partnering with cross-functional leaders to prioritize use cases, assess vendors, strengthen engineering quality, and deliver measurable outcomes.
Work model
We believe hybrid work is the way forward as we strive to provide flexibility wherever possible. Based on this role's business requirements, this is a hybrid position requiring 3 days a week in a client or Cognizant office in Atlanta, Georgia. Regardless of your working arrangement, we are here to support a healthy work-life balance through our various wellbeing programs.
The working arrangements for this role are accurate as of the date of posting. This may change based on the project you're engaged in, as well as business and client requirements. Rest assured, we will always be clear about role expectations.
What you must have to be considered
- 12+ years of software, platform, data, or cloud engineering experience, including 5+ years in architecture or technical leadership, with demonstrated delivery of enterprise-scale production AI systems.
- Hands-on expertise in LLM application engineering, agents, retrieval-augmented generation, evaluation, guardrails, distributed systems, cloud architecture, and multiple model providers.
- Experience establishing secure, reliable AI platform and LLMOps practices, including CI/CD, observability, automated evaluation, incident response, privacy, security, governance, resilience, and cost optimization.
- Experience guiding modern full-stack and distributed-system engineering using Angular and TypeScript, C# and .NET, APIs and events, PostgreSQL or AlloyDB, Redis, vector or graph stores, containers, and Google Cloud services.
- Strong executive communication, client-facing leadership, cross-functional collaboration, mentoring, and technical decision-making skills.
These will help you succeed
- Experience with LangGraph, Semantic Kernel, LangChain, LlamaIndex, GraphRAG, or comparable frameworks.
- Experience applying AI-native and specification-driven engineering practices, including coding agents, automated testing, security checks, validation gates, architecture decisions, and requirement-to-code traceability.
- Ability to assess emerging AI technologies and vendors, manage technical and business tradeoffs, and connect architecture decisions to measurable outcomes.
- Ability to build engineering standards, lead design and code reviews, and develop communities of practice.
- Experience operating in complex consulting or enterprise environments with accountability for delivery quality, reliability, security, and cost.
We're excited to meet people who share our mission and can make an impact in a variety of ways. Don't hesitate to apply, even if you only meet the minimum requirements listed.
Compensation
$122,400 - $223,500
This position is eligible for Cognizant's discretionary annual incentive program, based on performance and subject to the terms of Cognizant's applicable plans.
Benefits
- Medical, dental, vision and life insurance
- 401(k) plan and contributions
- Employee stock purchase plan
- Employee assistance program
- 10 paid holidays plus PTO
- Paid parental leave and fertility assistance
- Learning and development certifications and programs
Salary Context
This $122K-$223K range is below the median for AI Architect roles in our dataset (median: $179K across 32 roles with salary data).
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 9,653 AI roles we're tracking, AI Architect positions make up 1% of the market. At Cognizant, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Architect roles pay a median of $230,000 based on 212 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($172K) sits 25% below the category median. Disclosed range: $122K to $223K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Cognizant AI Hiring
Cognizant has 35 open AI roles right now. They're hiring across AI Architect, AI/ML Engineer, AI Agent Developer, AI Engineering Manager. Positions span New York, NY, US, Minneapolis, MN, US, Dallas, TX, US. Compensation range: $100K - $223K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Architect roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (193) are outnumbered by mid-level (4,500) and senior (3,455) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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