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About This Role
AI Architect, AI \& Automation
About the Role
The applicant should have deep, hands\-on experience architecting, designing, and implementing enterprise\-grade AI, Machine Learning, and Generative AI solutions, and experience leading technical teams delivering complex AI and automation engagements across industries. Applicants should have some of the following experience:* Experience architecting and implementing AI/ML solutions across multiple domains, including:
+ Generative AI and Large Language Model (LLM) based solutions
+ Predictive and prescriptive Machine Learning models
+ Computer Vision
+ Natural Language Processing (NLP) and Conversational AI
+ Intelligent Document Processing (IDP / iOCR)
+ Agentic AI systems and multi\-agent orchestration.
- Worked across the end\-to\-end AI solution lifecycle: use case discovery, data assessment, solution architecture, model development, deployment, and monitoring.
- Experience translating business problems into AI solution architecture using design thinking and structured problem\-solving techniques.
- Hands\-on experience designing AI/ML platform architectures on cloud\-native and hybrid environments (AWS, Azure, GCP).
- Hands\-on experience with LLM frameworks and patterns (LangChain, LlamaIndex, Semantic Kernel), vector databases, and Retrieval\-Augmented Generation (RAG) architectures.
- Experience with MLOps/LLMOps practices, including model lifecycle management, CI/CD for ML, monitoring, and retraining pipelines.
- Strong hands\-on proficiency in Python and ML/AI frameworks such as TensorFlow, PyTorch.
- Experience with cloud AI/ML services such as Azure OpenAI, AWS Bedrock/SageMaker, and Google Cloud Vertex AI.
- Experience in data architecture and engineering to support AI initiatives, including data pipelines, feature stores, and data governance.
- Experience integrating AI solutions into enterprise systems and applications via APIs, microservices, and event\-driven architectures.
- Experience with AI governance, responsible AI practices, model risk management, and data privacy/security considerations.
- Experience integrating AI/GenAI capabilities with automation platforms (e.g., UiPath, Power Automate) to enable intelligent automation.
- Travel to client sites and for practice work efforts is required on an as needed basis.
Additional Consulting Responsibilities* Client Relationship and Development: Leads team interactions with clients, including clients at senior levels. Anticipates and proactively addresses client’s needs. Earns client’s respect and appreciation.
- Client Delivery: Leads client delivery teams. Manages projects and drives projects to completion.
- Value and Expertise: Establishes focus area and concentrates deployment and delivery in that area. Establishes track record in focus area. Begins to contribute thought leadership and IP in focus area.
- People Development and Learning: Mentors and develops consultants on delivery teams. Ensures team members have skills needed to execute and deepen their expertise while on the project. Has counselees and meets them regularly. Helps them understand strengths and weaknesses, set realistic targets, and establish development plans that balance firm needs and personal aspirations.
- Consulting Behaviours: Develops focus area or specialization. Builds personal brand. Leads and mentors others. Cares about development of junior consultants and invests in their progression. Grows Infosys network outside of Consulting. Leverages relationships with other Infosys units to enhance client solutions and identify new opportunities for Consulting work. Reads situations and adjusts personal approach accordingly. Adopts behavior and language appropriate to the situation, to stay effective in different environments. Stays abreast of market developments in practice or discipline. Identifies threats and opportunities and positions to meet them; proactively learns new skills and abilities to stay relevant.
- Leadership and Firm Development: Leads delivery teams effectively, providing direction, guidance, motivation, course correction, and air cover as appropriate. Supports development of innovative thinking. Understands the Infosys Consulting business drivers and KPIs needed to build an effective and successful business. Represents Infosys through appropriate application of Infosys' sales and marketing materials/publications (service offerings, blog posts, etc.). Plays a key role in practice or firm\-building activities. Takes bottom\-line responsibility for firm building deliverables or activities.
- Sales: Supports Associate Partners and Partners in pursuit and proposal work. Identifies opportunities from client work and relationships; raises them to appropriate Associate Partner or Partner for action.
Basic Qualifications* Bachelor’s degree in computer science, Engineering, Data Science, or a related field, or foreign equivalent required.
- Cloud AI/ML certifications (e.g., AWS Certified Machine Learning, Azure AI Engineer, Google Cloud Professional ML Engineer)
- Minimum of 10 years of relevant work experience with 2 years of experience in comparable consulting services.
- Strategic mindset and the ability to lead and develop other team members.
- Multitask, engage with stakeholders, plan effectively, and consistently achieve operational goals.
- Excellent relationship\-building abilities.
- Ability to collaborate with resources in global delivery model.
- Experience in leading programs using Agile and/or hybrid methodologies.
- U.S. and Canadian citizens and those authorized to work in the U.S. and Canada are encouraged to apply. Infosys will not sponsor H\-1B or other work authorization for this role at this time.
Preferred Qualifications* MBA or equivalent advanced degree, Industry\-related certification preferred.
- Creative problem solver
- Strategic mindset and the ability to collaborate with other team members
For candidates based out of CA, WA, NY, IL, MN,NJ states, estimated annual gross compensation range is $154,375 to $193,125
Along with competitive pay, as a full\-time Infosys employee you are also eligible for the following benefits:\-* + Medical/Dental/Vision/Life Insurance
+ Long\-term/Short\-term Disability
+ Health and Dependent Care Reimbursement Accounts
+ Insurance (Accident, Critical Illness, Hospital Indemnity, Legal)
+ 401(k) plan and contributions dependent on salary level
+ Paid holidays plus Paid Time Off
Salary Context
This $154K-$193K range is below the median for AI Architect roles in our dataset (median: $197K across 33 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 3,708 AI roles we're tracking, AI Architect positions make up 1% of the market. At Infosys, 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 $254,798 based on 67 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($173K) sits 32% below the category median. Disclosed range: $154K to $193K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Infosys AI Hiring
Infosys has 8 open AI roles right now. They're hiring across AI Architect, RAG Engineer, AI/ML Engineer, AI Consultant. Positions span Richardson, TX, US, Alpharetta, GA, US, Basking Ridge, NJ, US. Compensation range: $146K - $193K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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