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About This Role
Job Description
-------------------
#### Requisition ID
94420
#### Department
Tech Data AI Ventures
#### Job Function
Tech Data AI Ventures
#### Location
New York,New York,United States
#### Role Location Designation
Hybrid \- 3 days per week
Location Designation: Hybrid \- 3 days per week
Role Overview
As the Enterprise AI Architect, you will serve as the technical anchor for the platform and the most senior individual contributor on the team and also AI architect for AIGC. You will define the engineering patterns, reference architectures, and reusable platform capabilities that every AI solution built across the organization will inherit.
This role is responsible for designing and building the platform's most complex components—including AI lifecycle registries, control\-plane services, harness and memory capabilities, and the engineering foundations that enable teams to rapidly develop, deploy, and govern agentic AI solutions. You'll partner closely with the AI Platform Lead while providing technical mentorship across the engineering team through architecture reviews, engineering standards, and hands\-on implementation.
This is a deeply technical role for an engineer who enjoys solving difficult distributed systems problems across cloud infrastructure, platform engineering, AI runtimes, and software architecture. The platform is built on Google Cloud and leverages modern cloud\-native capabilities while maintaining portability through open standards and reusable engineering patterns.
Our Engineering Principles
Our team is intentionally small, senior, and highly technical. Regardless of title, every engineer is expected to build AI systems—and build with AI.
- Build agents that power the platform. Develop platform capabilities as intelligent agents—not just traditional services. Examples include lifecycle management agents that register, version, monitor, govern, and retire AI assets across the enterprise.
- Build cloud agents that plan and implement. Create agents that can translate business needs into implementation plans, orchestrate the required skills and tooling, and execute work with human oversight at the appropriate checkpoints.
- Build end\-to\-end multi\-agent solutions. Design and implement solutions where specialized agents collaborate to architect systems, provision infrastructure, generate code, validate through dedicated testing agents, deploy applications, perform post\-deployment verification, and maintain complete operational traceability.
- Build with AI\-assisted engineering tools. Be fluent with modern AI development tools such as Cursor, Claude Code, GitHub Copilot, Windsurf, or equivalent technologies. AI\-assisted software development is a core engineering competency and will be evaluated throughout the interview process.
What You'll Do
- Define and evolve the enterprise reference architecture and reusable engineering patterns that underpin every AI asset, including common registration schemas, semantic metadata, execution contracts, governance controls, and lifecycle management.
- Design and build the platform's most technically challenging components, including AI lifecycle registries, AI control\-plane services, model abstraction layers, enterprise memory services, retrieval capabilities, and reusable platform APIs.
- Develop the engineering foundations that enable rapid delivery of agentic AI solutions, including reusable scaffolds, frameworks, builder agents, and multi\-agent implementation patterns.
- Deliver platform capabilities as intelligent agents where appropriate, enabling the platform to automate its own lifecycle management, planning, governance, and operational workflows.
- Establish engineering standards for software quality, testing, CI/CD, Infrastructure as Code, observability, security\-by\-default, and AI\-assisted software development across the platform team.
- Drive platform portability through open standards, containerization, standardized telemetry, metadata, model serving, and cloud\-native engineering practices that avoid unnecessary vendor lock\-in.
- Review complex designs and production code, mentor senior engineers, and help establish a culture of technical excellence across the organization.
- Build production\-quality software while leveraging AI\-assisted engineering throughout the software development lifecycle.
What You'll Bring
Required Skills
- Significant experience designing and building production\-scale software platforms, developer platforms, AI platforms, or distributed systems in cloud\-native environments.
- Deep expertise in full\-stack software engineering, including backend services, APIs, distributed systems, and modern engineering practices, with the ability to own systems through production operations.
- Strong experience with cloud infrastructure and platform engineering, including Google Cloud Platform (preferred), Kubernetes, containers, Infrastructure as Code (Terraform), CI/CD, identity, networking, observability, and multi\-tenant architectures.
- Hands\-on expertise building production generative AI and agentic systems, including LLM runtimes, retrieval\-augmented generation (RAG), memory architectures, multi\-agent orchestration, Model Context Protocol (MCP), agent\-to\-agent (A2A) communication, and evaluation frameworks.
- Experience designing secure, governed AI platforms, including identity\-aware access controls, runtime guardrails, policy enforcement, auditability, observability, and responsible AI practices.
- Strong software engineering background with expert\-level Python and experience with one or more additional languages such as Go, Java, or TypeScript.
- Demonstrated experience using AI\-assisted software development tools such as Cursor, Claude Code, GitHub Copilot, Windsurf, or similar technologies as part of modern engineering workflows.
- Proven ability to influence engineering direction through technical leadership, architecture reviews, mentoring, and engineering best practices.
Preferred Skills
- Experience building enterprise AI platforms within financial services, insurance, or another highly regulated industry.
- Experience with Vertex AI, LangChain, Google ADK, AutoGen, CrewAI, MLflow, OpenTelemetry, Ray, vLLM, DeepSpeed, Delta UniForm, Apache Iceberg, or related AI platform technologies.
- Experience building reusable developer platforms, internal engineering frameworks, or platform engineering capabilities adopted across multiple teams.
- Knowledge of enterprise AI governance frameworks including NIST AI RMF, ISO 42001, SOC 2, HIPAA, GDPR, or emerging AI regulations.
Why This Role
- Own the platform's foundation. Design and build the reusable architecture, services, and engineering patterns that every enterprise AI solution will inherit.
- Solve genuinely difficult engineering problems. Work across AI control planes, enterprise memory, distributed systems, multi\-agent orchestration, and platform engineering at enterprise scale.
- Stay deeply technical. This is a Principal Engineer role with significant technical ownership and influence, allowing you to shape architecture and engineering direction while remaining hands\-on.
- Work alongside exceptional engineers. Join a small, senior engineering team focused on solving complex problems with high autonomy, minimal bureaucracy, and a strong engineering culture.
- Create lasting impact. The platform capabilities you build will become the engineering foundation for AI across New York Life.
Location
We are committed to attracting exceptional talent and will consider flexible work arrangements for the right candidate.
Pay Transparency
Salary Range: $147,500\-$211,000
Overtime eligible: Exempt
Discretionary bonus eligible: Yes
Sales bonus eligible: No
Actual base salary will be determined based on several factors but not limited to individual’s experience, skills, qualifications, and job location. Additionally, employees are eligible for an annual discretionary bonus. In addition to base salary, employees may also be eligible to participate in an incentive program.
Company Overview
At New York Life, our 180\-year legacy of purpose and integrity fuels our future. As we evolve into a more technology\-, data\-, and AI\-enabled organization, we remain grounded in the values that drive lasting impact.
Our diverse business portfolio creates opportunities to make a difference across industries and communities—inviting bold thinking, collaborative problem\-solving, and purpose\-driven innovation. Here, you’ll find the rare balance of long\-standing stability and forward momentum, supported by an inclusive team that honors tradition while embracing progress.
As a Fortune 100 mutual company, we offer a place to grow your skills, contribute to meaningful work, and deliver solutions that matter. Your ideas drive what’s next, and your growth powers it.
Our Benefits
We provide a full package of benefits for employees – and have unique offerings for a modern workforce, including leave programs, adoption assistance, and student loan repayment programs. Based on feedback from our employees, we continue to refine and add benefits to our offering, so that you can flourish both inside and outside of work.Click hereto discover more about our comprehensive benefit options or visit our NYL Benefits Site.
Our Commitment to Inclusion
At New York Life, fostering an inclusive workplace is fundamental to who we are and how we serve our communities. We have a longstanding commitment to creating an environment where individuals can contribute their best and succeed together. This foundation is rooted in our core values of humanity and integrity, ensuring that every employee feels valued and supported. By embracing a broad range of perspectives and experiences, we achieve greater success and fulfill our promise of providing financial security and peace of mind to families across all communities. Click here to learn more about New York Life’s leadership in this space.
Recognized as one of *Fortune’s* World’s Most Admired Companies, New York Life is committed to improving local communities through a culture of employee giving and volunteerism, supported by the Foundation. We're proud that due to our mutuality, we operate in the best interests of our policy owners. To learn more about career opportunities at New York Life, please visit the Careers page of www.NewYorkLife.com.
Visit our LinkedIn to see how our employees and agents are leading the industry and impacting communities.
Visit our Newsroom to learn more about how our company is constantly evolving to meet our clients' and employees’ needs.
Job Requisition ID: 94420
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Salary Context
This $147K-$211K range is below the median for AI Architect roles in our dataset (median: $181K across 29 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 4,317 AI roles we're tracking, AI Architect positions make up 1% of the market. At New York Life, 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 $237,300 based on 102 positions with disclosed compensation. This role's midpoint ($179K) sits 24% below the category median. Disclosed range: $147K to $211K.
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.
New York Life AI Hiring
New York Life has 15 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, Data Scientist, Data Engineer. Positions span New York, NY, US, White Plains, NY, US. Compensation range: $72K - $230K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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
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