Agentic AI Architect / Lead - Snowflake Cortex

Irving, TX, US Senior AI Architect

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Skills & Technologies

AnthropicAutogenAwsAzureClaudeCrewaiEmbeddingsFivetranGcpGemini

About This Role

AI job market dashboard showing open roles by category

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.

Job Location : Arlington, VA (Onsite/Hybrid from Day 1\)

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Job Description

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We are seeking a highly experienced Agentic AI Architect / Lead with deep hands\-on expertise in Snowflake Cortex, AI Agents, Generative AI, RAG architectures, and enterprise AI solutions. This role will be responsible for designing, evaluating, optimizing, and scaling Agentic AI platforms while providing technical leadership to a team of engineers, architects, and AI practitioners.

The ideal candidate will have proven experience building AI agents using Snowflake Cortex capabilities, implementing enterprise\-grade GenAI solutions, and driving AI adoption through best practices, governance, and architecture standards. The candidate should be capable of quickly assessing existing AI agents, identifying improvement opportunities, and delivering scalable Agentic AI frameworks aligned with business objectives.

Key Responsibilities

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  • Lead the architecture, design, and implementation of enterprise\-scale Agentic AI solutions using Snowflake Cortex.
  • Evaluate existing AI agents and autonomous workflows, identify performance gaps, and recommend improvements for scalability, reliability, and business value.
  • Design and implement multi\-agent architectures, orchestration frameworks, and intelligent automation workflows.
  • Architect AI\-powered applications leveraging Snowflake Cortex capabilities, including:
  • Cortex Search
  • Cortex Analyst
  • Cortex LLM Functions
  • Document AI
  • Vector Embeddings
  • Semantic Search
  • RAG (Retrieval\-Augmented Generation)
  • Establish best practices for prompt engineering, agent memory, tool usage, context management, reasoning, and AI governance.
  • Lead technical decision\-making for Agentic AI frameworks such as LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar platforms.
  • Design semantic layers and vector\-based retrieval solutions to support enterprise AI applications.
  • Mentor and guide engineering teams on AI agent design patterns, evaluation methodologies, and deployment strategies.
  • Define AI agent monitoring, performance metrics, evaluation frameworks, and governance standards.

Required Qualifications

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  • 8\+ years of experience in Data Engineering, AI/ML, Data Architecture, or Enterprise Technology Solutions.
  • 5\+ years of hands\-on experience building and deploying Generative AI and Agentic AI solutions.
  • Strong practical experience with Snowflake Cortex including:
  • Cortex Search
  • Cortex Analyst
  • Vector Embeddings
  • Semantic Search
  • RAG Architectures
  • Proven experience designing and implementing AI Agents and Agentic AI frameworks in enterprise environments.
  • Deep understanding of:
  • Autonomous Agents
  • Agent Orchestration
  • Prompt Engineering
  • Experience building conversational AI, AI copilots, intelligent assistants, and workflow automation solutions.
  • Strong understanding of vector databases, embeddings, semantic search, and knowledge retrieval architectures.
  • Hands\-on experience with Python, SQL, Snowpark, and API integrations.

Preferred Qualifications

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  • Experience with LangGraph, CrewAI, AutoGen, Semantic Kernel, LangChain, or similar Agentic AI frameworks.
  • Experience building AI copilots, autonomous business workflows, and enterprise AI platforms.
  • SnowPro Certifications (Core, Advanced, or Architect).
  • Experience with Azure OpenAI, OpenAI, Anthropic Claude, Gemini, or other LLM ecosystems.
  • Experience with AWS, Azure, or GCP cloud platforms.
  • Knowledge of MLOps, LLMOps, AI observability, and governance frameworks.
  • Experience leading enterprise AI transformation initiatives.
  • Understanding of modern data stack technologies such as dbt, Airflow, Kafka, Spark, Fivetran, or Matillion.

The base compensation range for this role in the posted location is: 90786\- 120673

Capgemini provides compensation range information in accordance with applicable national, state, provincial, and local pay transparency laws. The base compensation range listed for this position reflects the minimum and maximum target compensation Capgemini, in good faith, believes it may pay for the role at the time of this posting. This range may be subject to change as permitted by law.

The actual compensation offered to any candidate may fall outside of the posted range and will be determined based on multiple factors legally permitted in the applicable jurisdiction.

These may include, but are not limited to: Geographic location, Education and qualifications, Certifications and licenses, Relevant experience and skills, Seniority and performance, Market and business consideration, Internal pay equity.

It is not typical for candidates to be hired at or near the top of the posted compensation range.

In addition to base salary, this role may be eligible for additional compensation such as variable incentives, bonuses, or commissions, depending on the position and applicable laws.

Capgemini offers a comprehensive, non\-negotiable benefits package to all regular, full\-time employees. In the U.S. and Canada, available benefits are determined by local policy and eligibility and may include:

  • Paid time off based on employee grade (A\-F), defined by policy: Vacation: 12\-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave
  • Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
  • Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
  • Life and disability insurance
  • Employee assistance programs
  • Other benefits as provided by local policy and eligibility

Important Notice: Compensation (including bonuses, commissions, or other forms of incentive pay) is not considered earned, vested, or payable until it becomes due under the terms of applicable plans or agreements and is subject to Capgemini’s discretion, consistent with applicable laws. The Company reserves the right to amend or withdraw compensation programs at any time, within the limits of applicable legislation.

Disclaimers

Capgemini is an Equal Opportunity Employer encouraging inclusion in the workplace. Capgemini also participates in the Partnership Accreditation in Indigenous Relations (PAIR) program which supports meaningful engagement with Indigenous communities across Canada by promoting fairness, accessibility, inclusion and respect. We value the rich cultural heritage and contributions of Indigenous Peoples and actively work to create a welcoming and respectful environment. All qualified applicants will receive consideration for employment without regard to race, national origin, gender identity/expression, age, religion, disability, sexual orientation, genetics, veteran status, marital status or any other characteristic protected by law.

This is a general description of the Duties, Responsibilities and Qualifications required for this position. Physical, mental, sensory or environmental demands may be referenced in an attempt to communicate the manner in which this position traditionally is performed. Whenever necessary to provide individuals with disabilities an equal employment opportunity, Capgemini will consider reasonable accommodations that might involve varying job requirements and/or changing the way this job is performed, provided that such accommodation does not pose an undue hardship. Capgemini is committed to providing reasonable accommodation during our recruitment process. If you need assistance or accommodation, please reach out to your recruiting contact.

Please be aware that Capgemini may capture your image (video or screenshot) during the interview process and that image may be used for verification, including during the hiring and onboarding process.

Capgemini is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society. It is a responsible and diverse group of 340,000 team members in more than 50 countries. With its strong over 55\-year heritage, Capgemini is trusted by its clients to unlock the value of technology to address the entire breadth of their business needs. It delivers end\-to\-end services and solutions leveraging strengths from strategy and design to engineering, all fueled by its market leading capabilities in AI, generative AI, cloud and data, combined with its deep industry expertise and partner ecosystem.

Role Details

Company Capgemini
Title Agentic AI Architect / Lead - Snowflake Cortex
Location Irving, TX, US
Category AI Architect
Experience Senior
Salary Not disclosed
Remote No

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 4,317 AI roles we're tracking, AI Architect positions make up 1% of the market. At Capgemini, 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

Anthropic (6% of roles) Autogen (3% of roles) Aws (28% of roles) Azure (22% of roles) Claude (12% of roles) Crewai (3% of roles) Embeddings (7% of roles) Fivetran Gcp (15% of roles) Gemini (5% of roles)

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 $237,300 based on 102 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.

Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.

Capgemini AI Hiring

Capgemini has 20 open AI roles right now. They're hiring across AI Architect, Prompt Engineer, AI/ML Engineer, Data Engineer. Positions span Chicago, IL, US, Alpharetta, GA, US, New York, NY, US. Compensation range: $65K - $191K.

Location Context

Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.

The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.

Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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

Based on 102 roles with disclosed compensation, the median salary for AI Architect positions is $237,300. Actual compensation varies by seniority, location, and company stage.
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.
About 15% of the 4,317 AI roles we track offer remote work. Remote availability varies by company and seniority level, with senior and leadership roles more likely to offer location flexibility.
Capgemini is among the companies actively hiring for AI and ML talent. Check our company profiles for detailed breakdowns of open roles, salary ranges, and hiring trends.
Common next steps from AI Architect positions include Senior Engineer, AI Architect, Engineering Manager, Principal Engineer. Progression depends on whether you lean toward technical depth, people management, or product strategy.

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