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About This Role
About the role
We are seeking an experienced AI Agent Solutions Architect to design and deliver enterprise-scale AI solutions leveraging AWS AI services, Amazon Bedrock, and modern agentic frameworks. In this role, you will lead the architecture of AI-powered applications, collaborate with cross-functional teams, and drive best practices for scalable, secure, and production-ready AI solutions. This is an exciting opportunity to shape the future of enterprise AI through innovative agent-based and Generative AI technologies.
In this role, you will:
- Design and architect enterprise-scale AI Agent solutions using AWS AI services, including Amazon Bedrock.
- Lead the development of agentic and multi-agent workflows using Python and modern AI orchestration frameworks.
- Design and implement RAG-based solutions leveraging enterprise data sources, vector databases, and knowledge repositories.
- Define AI architecture best practices for scalability, security, observability, governance, and production deployment.
- Partner with business and engineering teams to deliver AI-driven solutions while providing technical leadership and mentorship.
Work model: Hybrid
We strive to provide flexibility wherever possible. Based on this role's business requirements, this is a Hybrid position in Miami, FL
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 need to have to be considered:
- 8+ years of experience in software engineering, solution architecture, AI engineering, or related disciplines with strong Python development expertise.
- Proven experience building AI/GenAI solutions using AWS AI services, including Amazon Bedrock, and agent orchestration frameworks such as Strands Agents.
- Deep knowledge of LLMs, RAG, prompt engineering, embeddings, vector databases, and agentic AI architectures.
- Experience designing APIs, microservices, cloud-native applications, and working with relational, NoSQL, and vector database technologies.
- Strong communication and leadership skills with the ability to influence architecture decisions and collaborate with cross-functional teams.
These will help you stand out:
- Experience designing and deploying enterprise-scale AI Agent platforms in production environments.
- AWS certifications such as AWS Certified Solutions Architect or AWS Certified Machine Learning Engineer.
- Experience with advanced multi-agent orchestration, AI governance, model monitoring, and responsible AI practices.
- Knowledge of enterprise integrations, AI security frameworks, and large-scale cloud architectures.
- Experience leading architecture reviews, mentoring teams, and driving AI transformation initiatives.
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. Think about your transferable experiences and unique skills that make you stand out as someone who can bring new and exciting things to this role.
Important Disclaimers:
Please note: A few of our roles may require in-person interviews at Cognizant offices or client locations, depending on project or client needs.
Candidate must be legally authorized to work in the United States without the need for employer sponsorship, now or at any time in the future
Salary and compensation:
The annual salary for this position will be between $138,500 to$162,500 per year depending on the experience and other qualifications of the successful candidate. This position is also eligible for Cognizant’s discretionary annual incentive program and stock awards, based on performance and subject to the terms of Cognizant’s applicable plans.
Applications will be received by October 29th, 2026.
Benefits: Cognizant offers the following benefits for this position, subject to applicable eligibility requirements:
- Medical/Dental/Vision/Life Insurance.
- Paid holidays plus Paid Time Off.
- 401(k) plan and contributions.
- Long-term/Short-term Disability.
- Paid Parental Leave.
- Employee Stock Purchase Plan.
Disclaimer: The salary, other compensation, and benefits information is accurate as of the date of this posting. Cognizant reserves the right to modify this information at any time, subject to applicable law.
Salary Context
This $138K-$162K range is in the lower quartile for AI Agent Developer roles in our dataset (median: $190K across 12 roles with salary data).
View full AI Agent Developer salary data →Role Details
About This Role
AI Agent Developers build autonomous systems that can reason, plan, and take actions. They design multi-step workflows, tool-use frameworks, and orchestration layers that let LLMs interact with external systems. This is the frontier of applied AI engineering.
Agent development is where the most interesting (and hardest) problems in applied AI live right now. Making an LLM answer a question is straightforward. Making it reliably execute a 15-step workflow that involves calling APIs, reading databases, making decisions, and recovering from errors is an unsolved problem. You're building systems that have to work despite the fact that the underlying model is non-deterministic.
Across the 8,943 AI roles we're tracking, AI Agent Developer positions make up 1% of the market. At Cognizant, this role fits into their broader AI and engineering organization.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
What the Work Looks Like
A typical week includes: designing the action space and tool definitions for a new agent use case, debugging why the agent chose the wrong action sequence on a specific input, building evaluation frameworks that test agent reliability across hundreds of scenarios, optimizing the prompt chain for cost and latency, and implementing safety guardrails to prevent the agent from taking destructive actions. The work is equal parts engineering and empirical science.
AI Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
Skills Required
Deep experience with LLM APIs and agent frameworks (LangChain, CrewAI, AutoGen). Strong understanding of prompt engineering, function calling, and error handling for non-deterministic systems. Python is standard. Experience with orchestration patterns, state management, and workflow engines adds significant value.
The best agent developers think like systems engineers. They design for failure modes, build observability into every step, and understand that agent reliability is the product. Expertise in evaluation methodology for non-deterministic systems is the differentiator. Can you measure whether your agent works 'well enough'? Can you find the edge cases where it breaks?
Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
Compensation Benchmarks
AI Agent Developer roles pay a median of $214,400 based on 180 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($150K) sits 30% below the category median. Disclosed range: $138K to $162K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
Cognizant AI Hiring
Cognizant has 34 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, Minneapolis, MN. Compensation range: $100K - $223K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI Agent Developer roles include Software Engineer, LLM Engineer, Prompt Engineer.
From here, career progression typically leads toward AI Architect, Principal Engineer, Head of AI Engineering.
Build agents. That's the portfolio. Take an open-source agent framework, build something that completes a non-trivial multi-step task, evaluate it rigorously, and document what you learned about reliability, cost, and failure modes. The field is new enough that practical experience counts for more than credentials.
What to Expect in Interviews
Interviews focus on systems thinking and reliability engineering. Expect questions about agent architecture: how you'd design a multi-step workflow with error recovery, how you'd evaluate agent performance, and how you'd prevent agents from taking destructive actions. Coding exercises often involve building a simple agent with tool use and evaluating its behavior across different scenarios. Discussion of safety and guardrails is increasingly common.
When evaluating opportunities: Look for roles that describe specific agent use cases, mention evaluation methodology, and talk about production deployment. Early-stage companies exploring agents can be exciting, but be prepared for ambiguity. The most valuable roles are at companies that have already shipped a v1 and need to make it reliable.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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 Agent Developer is one of the newest and fastest-growing AI role categories. The market is early but accelerating as companies move beyond simple chatbots toward AI systems that can take real actions. Compensation is high because the skill set is rare and the business impact is potentially enormous.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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