Generative AI Analyst Jobs
Generative AI Analyst jobs are open across technology, financial services, healthcare, and media, from entry-level to senior and lead roles, with specializations in prompt engineering, model evaluation, and AI product analysis. Find a role that fits below and apply directly.
Find Generative AI Analyst JobsOverview
Showing 5 of 186+ Generative AI Analyst jobs
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Chicago, IL, USA; New York, NY, USA.
Minimum qualifications:
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- 5 years of experience with software development using Python or similar coding languages.
- Experience architecting AI systems on cloud platforms (e.g., GCP).
- Experience taking production-grade AI-driven solutions from conception to launch for customers.
- Experience leading technical discovery sessions with customers.
- Experience building pipelines for structured and unstructured data using both vector databases and retrieval-augmented generation (RAG)-like architectures to power enterprise AI solutions.
Preferred qualifications:
- Master’s degree or PhD in AI, Computer Science, or a related technical field.
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and patterns (e.g., ReAct, self-reflection, hierarchical delegation).
- Knowledge of LLM-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
About the job
As a GenAI Forward Deployed Engineer (FDE) at Google Cloud, you will be an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. Unlike traditional advisory roles, you will function as an innovator-builder, moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment.
This role is designed for high-agency engineers with a founder’s mindset. You will address blockers to production including solving the integration complexities, data readiness issues, and state-management issues that prevent AI from reaching enterprise-grade maturity. By embedding with accounts, you will serve a dual purpose: providing white glove deployment of AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Serve as a developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, model context protocol (MCP) servers) that drive measurable return on investment.
- Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters as part of an expert team.
- Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, and latency.
- Identify repeatable field patterns and friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.
- Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.
Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Chicago, IL, USA; New York, NY, USA.
Minimum qualifications:
- Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
- 5 years of experience with software development using Python or similar coding languages.
- Experience architecting AI systems on cloud platforms (e.g., GCP).
- Experience taking production-grade AI-driven solutions from conception to launch for customers.
- Experience leading technical discovery sessions with customers.
- Experience building pipelines for structured and unstructured data using both vector databases and retrieval-augmented generation (RAG)-like architectures to power enterprise AI solutions.
Preferred qualifications:
- Master’s degree or PhD in AI, Computer Science, or a related technical field.
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and patterns (e.g., ReAct, self-reflection, hierarchical delegation).
- Knowledge of LLM-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
About the job
As a GenAI Forward Deployed Engineer (FDE) at Google Cloud, you will be an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. Unlike traditional advisory roles, you will function as an innovator-builder, moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment.
This role is designed for high-agency engineers with a founder’s mindset. You will address blockers to production including solving the integration complexities, data readiness issues, and state-management issues that prevent AI from reaching enterprise-grade maturity. By embedding with accounts, you will serve a dual purpose: providing white glove deployment of AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Serve as a developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, model context protocol (MCP) servers) that drive measurable return on investment.
- Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters as part of an expert team.
- Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, and latency.
- Identify repeatable field patterns and friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.
- Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.
Generative AI Analyst Jobs by Experience Level
See All 186+ Generative AI Analyst Jobs
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Find Generative AI Analyst JobsGenerative AI Analyst Job Market
Who's Hiring
- Innodata30
- Google16
- TikTok13
- Amazon Web Services10
- Meta7
Top Industries Hiring
- Technology & Software23
- Artificial Intelligence3
- Distribution & Wholesale2
- Retail1
- Law & Legal Services1
What Employers Look For
The qualifications that appear most often in generative AI analyst jobs.
- Experience evaluating or fine-tuning large language models such as GPT or Gemini
- Proficiency in Python for data analysis, prompt scripting, or output processing
- Familiarity with retrieval-augmented generation architectures and vector databases
- Bachelor's degree in computer science, data science, linguistics, or a related field
- Ability to design and document structured prompt evaluation frameworks and rubrics
- Experience working cross-functionally with product, engineering, and data science teams
Tips for Your Generative AI Analyst Job Search
Tailor your resume for model evaluation
Hiring managers for generative AI analyst roles look for experience assessing LLM outputs, not just using them. Describe specific evaluation frameworks you used, the models you tested, and what you measured, whether that was accuracy, hallucination rate, or task completion.
Distinguish prompt engineering from AI engineering
Many listings conflate the two roles. Read each job description carefully and match your application language to theirs. If the role emphasizes structured prompting, retrieval-augmented generation, or output quality, lead with that work, not model training or infrastructure.
Apply early to roles that fit
Migrate Mate lists generative AI analyst openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Build a portfolio around documented outputs
Unlike traditional analyst roles, your portfolio should show prompt iteration logs, before-and-after output comparisons, or evaluation rubrics you designed. Concrete artifacts demonstrating how you improved a model's usefulness matter more than explaining what tools you know.
Prepare for a technical take-home exercise
Most generative AI analyst interviews include a live or take-home task where you write, test, or score prompts against defined criteria. Practice articulating why one output is better than another using specific, measurable language rather than subjective preference.
Negotiate scope alongside compensation
Generative AI analyst roles vary widely in autonomy, from executing defined tests to shaping an entire evaluation strategy. In final-round conversations, ask directly which models you will work with, what your decision-making authority covers, and how success is measured in the first year.
Generative AI Analyst Jobs: Frequently Asked Questions
Which companies are hiring the most generative ai analysts?
Innodata, Google, and TikTok are hiring the most generative ai analysts right now, with openings concentrated in California, Texas, and New York, based on current listings on Migrate Mate as of November 2026. Demand is especially active at technology firms building enterprise AI products and at financial services companies integrating large language models into existing workflows.
How many generative ai analyst jobs are remote?
About 79% of generative ai analyst openings are fully remote or hybrid as of November 2026, reflecting the digital nature of the work. Prompt engineering, model evaluation, and AI quality assurance roles tend to have the highest remote availability, while positions focused on cross-functional AI strategy or in-person stakeholder alignment are more likely to require on-site presence.
How do you become a generative ai analyst?
Start by building hands-on experience with large language model tools, focusing on prompt design, output evaluation, and iterative testing. Develop Python skills for scripting and data handling. Study evaluation methodologies including rubric design, hallucination detection, and benchmark frameworks. Document your work in a portfolio, then target roles at companies actively building or integrating AI products into their core operations.
Can you get hired as a generative ai analyst with no experience?
Yes, entry-level generative AI analyst roles exist, particularly at startups and AI product companies willing to train candidates with strong analytical foundations. Build a self-directed portfolio using open-source models, contribute to public AI evaluation projects or red-teaming initiatives, and frame transferable experience in research, linguistics, or data analysis in terms of output quality and structured reasoning.
What does the generative ai analyst interview process look like?
Most generative AI analyst interviews begin with a recruiter screen followed by a hiring manager conversation focused on your background with language models. A technical stage typically involves a take-home prompt engineering or evaluation task. Final rounds usually include a panel discussion with product or data science stakeholders and a presentation of your technical work, with questions on your reasoning process and quality criteria.
Where can I find and apply to generative ai analyst jobs?
You can find and apply to generative AI analyst jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your background and specialization, then apply directly to each one that fits.
See All 186+ Generative AI Analyst Jobs
Find roles that match your experience and apply in just a few clicks.
Find Generative AI Analyst Jobs