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About This Role
The Senior AI/ML Architect is a visionary leader responsible for defining and delivering scalable, innovative AI solutions for Gas Power Controls. This role entails architecting systems that leverage advanced AI to solve complex business problems and enable transformative applications. You will lead development of products supporting power producing customers and support enterprise\-scale AI initiatives leveraging Bedrock foundational models, Azure OpenAI, Google Gemini, and Open Weight models. The core platform is based on AWS, with additional integrations into Azure for specific AI use cases. The Senior AI/ML Architect works closely with product owners, data scientists, and software development teams to design frameworks, deploy applications, and ensure seamless integration with enterprise systems. As a technical authority, this role emphasizes system scalability, high performance, security, and ethical considerations. You will guide teams in adopting generative AI technologies, mentor engineering teams, and drive innovation to deliver a competitive edge. GE Vernova Gas Power Controls is driving strategy and leading product efforts to execute on the business’s mission to help power the energy transition. We forge the collaborations and help invent the technologies required to electrify and decarbonize for a zero\-carbon future.
Job Description
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Responsibilities:
- Architect and oversee the development of robust, scalable systems using generative AI models.
- Collaborate with stakeholders to define business requirements and technical specifications for generative AI applications.
- Guide the selection, customization, and optimization of state\-of\-the\-art generative AI models.
- Design a system to maintain deployed solutions at customer sites.
- Develop end\-to\-end pipelines for inference and monitoring in production environments.
- Ensure systems meet high standards for performance, scalability, and security while adhering to data privacy regulations.
- Lead the implementation of APIs, microservices, and frameworks to integrate AI models into enterprise solutions.
- Design scalable and efficient architectures for generative AI models, applications, and workflows.
- Ensure seamless integration of Generative AI capabilities into existing systems.
- Provide mentorship to engineering teams, fostering expertise in AI and software architecture.
- Design and maintain platforms to handle large\-scale solution applications in collaboration with legal/compliance teams
- Define architectural best practices to mitigate risks associated with Generative AI (e.g., model hallucinations)
- Align AI architecture with organizational goals and contribute to strategic technology roadmaps.
- Document architectural designs, workflows, and decisions for transparency and scalability.
Required:
- Bachelor’s degree or higher in a relevant discipline.
- 8\+ years of experience within software engineering or a related field.
- Authorized to work in the United States; sponsorship is not supported for this role.
Desired:
- Deep understanding of LLM integration patterns (RAG, Agents, Tool\-use) and Prompt Engineering strategies.
- Expertise in designing scalable, distributed architectures for AI systems.
- Strong experience with cloud computing platforms (AWS, Azure, GCP) and containerization (Kubernetes, Docker).
- Knowledge of on\-prem/disconnected deployments, containerization on bare metal, and hardware constraints.
- Familiarity with large\-scale distributed systems and database technologies.
- Experience in creating technical design documents and implementation playbooks for target\-state AI solutions within cloud environments based on
- Experience translating business requirements into technical solution designs
- Thorough understanding of integration platforms and protocols (e.g., REST, SOAP, HTTP, UDP, ETC.)
- Proficiency in designing RESTful APIs and GraphQL endpoints for AI services.
- Knowledge of API development, microservices architecture, and DevOps practices.
- Proficiency in MLOps/LLMOps and model lifecycle management, including CI/CD pipelines for training, testing, and deploying AI models at scale.
- Performance optimization for AI/ML workloads, including GPU/TPU acceleration, model quantization, pruning, and distillation.
- Observability \& Monitoring of AI pipelines, encompassing logging, tracing, and metrics to detect drift, anomalies, or performance bottlenecks.
- Security, Privacy, and Compliance knowledge, with an understanding of data governance (GDPR, HIPAA, SOC 2\) and secure model serving.
- Proven track record of designing scalable Generative AI use case solutions.
- Exceptional leadership, strategic thinking, and problem\-solving abilities.
- Excellent communication skills for engaging with stakeholders across technical and business domains.
- Strong oral and written communication skills.
- Strong interpersonal and leadership skills.
- Demonstrated ability to analyze and resolve problems.
- Demonstrated ability to lead programs / projects.
- Ability to document, plan, market, and execute programs.
Additional Information
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GE Vernova offers a great work environment, professional development, challenging careers, and competitive compensation. GE Vernova is an Equal Opportunity Employer. Employment decisions are made without regard to race, color, religion, national or ethnic origin, sex, sexual orientation, gender identity or expression, age, disability, protected veteran status or other characteristics protected by law.
GE Vernova will only employ those who are legally authorized to work in the United States for this opening. Any offer of employment is conditioned upon the successful completion of a drug screen (as applicable).
Relocation Assistance Provided: No
For candidates applying to a U.S. based position, the pay range for this position is between $131,700\.00 and $219,300\.00\. The Company pays a geographic differential of 110%, 120% or 130% of salary in certain areas. The specific pay offered may be influenced by a variety of factors, including the candidate’s experience, education, and skill set.
Bonus eligibility: discretionary annual bonus.
This posting is expected to remain open for at least seven days after it was posted on July 13, 2026\.
Available benefits include medical, dental, vision, and prescription drug coverage; access to Health Coach from GE Vernova, a 24/7 nurse\-based resource; and access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services. Retirement benefits include the GE Vernova Retirement Savings Plan, a tax\-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants. Other benefits include tuition assistance, adoption assistance, paid parental leave, disability benefits, life insurance, 12 paid holidays, and permissive time off.
GE Vernova Inc. or its affiliates (collectively or individually, “GE Vernova”) sponsor certain employee benefit plans or programs GE Vernova reserves the right to terminate, amend, suspend, replace, or modify its benefit plans and programs at any time and for any reason, in its sole discretion. No individual has a vested right to any benefit under a GE Vernova welfare benefit plan or program. This document does not create a contract of employment with any individual.
Salary Context
This $131K-$219K 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 GE Vernova, 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 ($175K) sits 31% below the category median. Disclosed range: $131K to $219K.
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.
GE Vernova AI Hiring
GE Vernova has 7 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Positions span Niskayuna, NY, US, Cambridge, MA, US, Remote, US. Compensation range: $108K - $276K.
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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