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About This Role
Job Description
About Persistent
We are an AI\-led, platform\-driven Digital Engineering and Enterprise Modernization partner, combining deep technical expertise and industry experience to help our clients anticipate what’s next. Our offerings and proven solutions create a unique competitive advantage for our clients by giving them the power to see beyond and rise above. We work with many industry\-leading organizations across the world, including 20 Fortune 50 companies and 4 of the 5 top banks in both the US and India, and numerous innovators across the healthcare ecosystem.
Our disruptor’s mindset, commitment to client success, and agility to thrive in the dynamic environment have enabled us to sustain our growth momentum. Persistent has been recognized across top industry platforms for innovation, leadership, and inclusion. We have delivered 23 sequential quarters of growth with $422\.5M in Q3 FY26 revenue, up 4\.0% Q\-o\-Q and 17\.3% Y\-o\-Y growth. Our 26,500\+ global team members, located in 18 countries, have been instrumental in helping the market leaders transform their industries. We won the 2025 ISG Star of Excellence™ Award for AI and Data Excellence and were named a Leader in the Everest Group Talent Readiness for Next\-generation Data, Analytics and AI Services PEAK Matrix® Assessment 2025\.
About Position:
We are seeking a highly experienced Agentic AI / Generative AI Architect with 15\+ years of software and solution architecture experience, combined with hands‑on expertise in Data Science, Machine Learning, and modern agent\-based AI systems. This role requires deep technical leadership in designing, implementing, and governing advanced multi\-agent AI ecosystems, RAG pipelines, cloud\-native AI platforms, and GenAI engineering practices. The ideal candidate will drive end‑to‑end solution architecture for enterprise\-grade AI applications while ensuring scalability, robustness, security, and operational excellence.
- Role: Gen AI Architect
- Job Location: Bridgewater, NJ
- Experience: 12 to 15 Years
- Job Type: Full Time Employment
What You'll Do:
- Design \& Implement Agentic AI Systems
- Architect and build multi\-agent, goal\-driven, autonomous AI systems using frameworks such as:
+ AutoGen
+ LangGraph
+ CrewAI
- Create intelligent agent ecosystems supporting orchestration, reasoning, and collaborative task execution.
- Prompt Engineering \& LLM Expertise
- Apply advanced prompt engineering techniques including:
+ Few\-shot prompting
+ Chain\-of\-thought reasoning
+ Prompt templates
- Optimize prompt flows for deterministic, scalable LLM\-driven systems
- Cloud\-Native AI Architecture
- Design and deploy AI/LLM systems on cloud platforms such as AWS Bedrock, Azure OpenAI, Google Vertex AI, etc.
- Ensure solutions meet enterprise NFRs including performance, security, cost\-optimization, and availability.
- RAG Pipelines, Vector Databases \& MCP
- Architect and deploy RAG pipelines using vector databases such as:
+ Pinecone
+ Weaviate
+ ChromaDB
+ FAISS
- Implement MCP Servers and Agent\-to\-Agent (A2A) communication frameworks.
- LMOPs / GenAIOPs
- Implement end\-to\-end operational pipelines for GenAI applications including:
+ Continuous integration \& deployment
+ Model monitoring \& drift detection
+ Logging, observability, and troubleshooting mechanisms
- Establish governance models, reusable patterns, and GenAI best practices.
- Application \& Microservices Architecture
- Design microservices\-based systems using Spring Boot, REST APIs, and secure API design patterns.
- Implement API security, versioning, and distributed system governance.
- Architect cloud\-native applications using AWS/Azure/GCP, Spring Cloud, PCF, or equivalent.
- Collaboration \& Leadership
- Work closely with Data Scientists, Product Owners, Business SMEs, and Engineering teams.
- Lead end\-to\-end solution architecture for enterprise AI initiatives.
- Conduct technical presentations, architectural reviews, and stakeholder communication.
Expertise You'll Bring:
- 5\+ years in software/solution architecture.
- Proven experience as a Data Scientist or ML Engineer with exposure to agentic AI systems.
- Experience designing multi\-agent systems using AutoGen, LangGraph, CrewAI, etc.
- Strong understanding of cloud AI platforms (Bedrock, Azure OpenAI, Vertex AI).
- Hands\-on experience with AI Code Assist tools such as:
+ GitHub Copilot
+ Windsurf
+ Cursor
+ AWS Q
- Expertise in Vector Databases, RAG pipelines, MCP, and multi\-agent communication.
- Strong proficiency in Python (preferred), and optionally Java/Node.js.
- Experience with microservices, Spring Boot, REST APIs, API security, and versioning.
- Proficiency in Docker, Kubernetes, CI/CD pipelines.
- Strong grasp of design patterns and architecture principles.
- Deep understanding of cloud\-native design and distributed systems.
- Experience designing AI systems that meet NFRs: scalability, security, performance, maintainability.
- Exceptional communication and presentation skills.
- Ability to articulate complex AI concepts to technical and non\-technical audiences.
- Strong leadership, problem\-solving mindset, and strategic thinking abilities.
- Ability to collaborate with cross\-functional teams to translate business needs into AI\-powered solutions.
Benefits:
- Competitive salary and benefits package
- Culture focused on talent development with quarterly growth opportunities and company\-sponsored higher education and certifications
- Opportunity to work with cutting\-edge technologies
- Employee engagement initiatives such as project parties, flexible work hours, and Long Service awards
- Annual health check\-ups
- Insurance coverage: group term life, personal accident, and Mediclaim hospitalization for self, spouse, two children, and parents
Values\-Driven, People\-Centric \& Inclusive Work Environment:
Persistent is dedicated to fostering diversity and inclusion in the workplace. We invite applications from all qualified individuals, including those with disabilities, and regardless of gender or gender preference. We welcome diverse candidates from all backgrounds.
- We support hybrid work and flexible hours to fit diverse lifestyles.
- Our office is accessibility\-friendly, with ergonomic setups and assistive technologies to support employees with physical disabilities.
- If you are a person with disabilities and have specific requirements, please inform us during the application process or at any time during your employment
Let’s unleash your full potential at Persistent \- persistent.com/careers
*“Persistent is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind.”*
1
Open Positions
AI,LangGraph,Vertex AI
Skills Required
Bridgewater, NJ, USA
Location
AI,LangGraph,Vertex AI,Other,NodeJS,Solution Architecture,Systems
Desirable Skills
12 to 15 years
Years Of Exp
166928
Job Code
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 26,159 AI roles we're tracking, AI Architect positions make up 1% of the market. At Persistent Systems, 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 $292,900 based on 108 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Persistent Systems AI Hiring
Persistent Systems has 8 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect, Data Scientist. Positions span US, Raleigh, NC, US, Los Angeles, CA, US. Compensation range: $170K - $170K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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