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About This Role
Why GM Financial Technology
Innovation isn’t just a talking point at GM Financial, it’s how we operate. From generative AI and cloud\-native technologies to peer\-led learning and hackathons, our tech teams are building real solutions that make a difference. We’re committed to AI\-powered transformation, using advanced machine learning and automation to help us reimagine customer interactions and modernize operations, positioning GM Financial as a leader in digital innovation within a dynamic industry.
Join us and discover a workplace where your ideas matter, your development is prioritized, and you can truly make a global impact.
About the role:
The Senior Data Analyst – Agentic AI \& GenAI Delivery plays a critical role in operationalizing and scaling Agentic AI solutions across the enterprise. This role focuses on driving delivery, deployment validation, and continuous optimization of AI systems through data\-driven insights, validation frameworks, and reporting mechanisms.Unlike traditional data analyst roles, this position operates at the intersection of AI systems, production delivery, and performance analytics, ensuring that Agentic AI solutions are functioning as intended, meeting business objectives, and operating reliably in production environments.
This role partners closely with architects, AI engineers, product teams, and business stakeholders to:
- Validate that AI use cases align with real\-world outcomes.
- Monitor agent behavior, performance, and reliability.
Establish data\-driven feedback loops for continuous improvement.
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The ideal candidate brings strong expertise in data analysis, AI system validation, observability, and reporting, along with a solid understanding of Agentic AI / GenAI workflows and production deployment challenges.
In this role you will:
- Drive the delivery and operational validation of Agentic AI solutions through structured data analysis and reporting.
- Define and implement data\-driven validation frameworks to evaluate AI system performance, accuracy, reliability, and business impact.
- Analyze production data from AI systems (agents, workflows, prompts, responses) to identify trends, issues, and optimization opportunities.
- Develop dashboards, reports, and metrics to track the health and effectiveness of Agentic AI deployments.
- Partner with architecture and engineering teams to validate feasibility outcomes and ensure solutions align with real\-world system behavior.
- Monitor AI systems in production, identifying anomalies, failure patterns, hallucinations, and performance degradation.
- Support deployment efforts by validating readiness criteria, including performance thresholds, guardrails, and compliance requirements.
- Enable continuous improvement loops by feeding insights back into model tuning, prompt design, and system architecture.
- Support A/B testing and experimentation for AI workflows and use cases.
- Collaborate with business stakeholders to measure and report on AI\-driven business outcomes and ROI.
- Ensure transparency and traceability of AI decisions through structured logging, trace analysis, and reporting.
- Contribute to the development of AI observability frameworks, including metrics, KPIs, and alerting strategies.
What makes You an ideal candidate?
Validate readiness of Agentic AI use cases for production deployment.
Track deployment success metrics and post\-production performance.
Identify gaps between expected vs. actual outcomes.
Define metrics for: Accuracy and response quality, Task completion success rates, Hallucination and failure cases, Latency and throughput.
Build evaluation datasets and validation pipelines.
Analyze: Agent workflows and decisions, Prompt\-response chains, Tool usage and orchestration behavior.
Develop observability dashboards using telemetry and logs.
Detect and escalate production issues and anomalies.
Data Analysis \& Reporting.
Perform root cause analysis on failures and performance issues.
Deliver executive\-level reporting on AI system effectiveness.
Provide actionable insights to improve system design and outcomes.
Work closely with: Lead Architects for feasibility alignment AI/ML engineers for model/system improvements Product teams for use case refinement.
Translate technical findings into clear business insights.
Advanced SQL, Python (Pandas, NumPy), or similar tools.
Data visualization platforms (Power BI, Tableau).
Strong experience in data validation, anomaly detection, and statistical analysis.
Familiarity with: LLM workflows and prompt engineering, RAG pipelines and evaluation strategies, Agent orchestration and tool integration.
Understanding of AI failure modes (hallucinations, drift, inconsistency).
Experience with: Logging, tracing, and telemetry systems AI evaluation tools and frameworks Monitoring production systems (Azure Monitor, Application Insights).
Strong working knowledge of Azure ecosystem, including: Azure OpenAI / AI services Azure Databricks Data platforms (Azure SQL, Cosmos DB) Monitoring tools (Log Analytics, App Insights).
Strong analytical and problem\-solving skills in complex AI\-driven systems.
Ability to connect system behavior with business outcomes.
Expertise in translating data into actionable insights.
High attention to detail in validation, quality, and accuracy.
Strong communication skills across technical and non\-technical stakeholders.
Ability to thrive in fast\-evolving AI environments.
Ability to wrangle large datasets, structured and non\-structured data, including data mining and manipulation.
Work Experience \& Education
6\-8 years experience in data analytics, data science, or AI Systems analysis or similar role required.
Experience supporting AI/ ML or GenAI systems in production environments preferred.
Auto finance experience preferred, cross functional Agile team experience preferred.
Bachelor’s Degree in Data Science, Computer Science, Engineering or related quantitative field preferred.
Master’s Degree in related quantitative field preferred.
What We Offer : Generous benefits package available on day one to include: 401K matching, bonding leave for new parents (12 weeks, 100% paid), tuition assistance, training, GM employee auto discount, community service pay and nine company holidays.
Our Culture: Our team members define and shape our culture — an environment that welcomes innovative ideas, fosters integrity, and creates a sense of community and belonging. Here we do more than work — we thrive.
Compensation: Competitive pay and bonus eligibility.
Work Life Balance: Hybrid work environment, 2\-days a week in office. The office locations for this role can be Irving, TX or Ft. Worth, TX
NOTE: We are unable to consider candidates who require visa sponsorship for this position
This position is not open to agency submissions
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\#LI\-MH1
\#GMFJobs
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At GM Financial, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $178,940 based on 11,900 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 $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
GM Financial AI Hiring
GM Financial has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Irving, TX, US.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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