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About This Role
Is the opportunity to join a culture where “We Do the Right Thing,” and “We Courageously Shape Our Future Together” important to you? If so, Guardian is seeking a motivated individual to join our team as a Finance Model \& AI Solutions Lead.
The colleague in this role will design, build, and scale intelligent planning solutions across Finance \& Risk. This role will combine Oracle Planning (EPM) model development with advanced AI agent capabilities to modernize forecasting, scenario analysis, and decision support.
The individual will directly build and enhance Oracle Planning models (metadata, business rules, drivers, integrations) while also developing and deploying AI\-powered agents that automate workflows, generate insights, and improve user experience. This role sits at the intersection of FP\\\&A, data, and AI, enabling a more dynamic, driver\-based, and insight\-led planning operating model powered by cutting\-edge artificial intelligence.
You will
Key Responsibilities
- Oracle Planning (EPM) Model Development \& AI Enhancement:
- Design, build, and maintain Oracle EPM Planning models with integrated AI capabilities for automated metadata generation and optimization
- Develop business rules, calculations, and driver\-based models enhanced by machine learning algorithms for improved accuracy
- Develop AI\-powered scenario generation capabilities that automatically create and evaluate multiple planning scenarios
- Implement AI\-assisted data validation and quality checks within EPM workflows
- Create and manage dimension hierarchies, forms, and dashboards with AI\-powered recommendations and anomaly detection
- Leverage AI code assistants to accelerate Groovy scripting and calculation development
- Partner with Data and Technology teams to build integrations between Oracle Planning and source systems
- Intelligent Analytics \& Decision Support:
- Implement machine learning models for predictive forecasting and trend analysis within the planning framework
- Build anomaly detection systems to identify unusual patterns in financial data and flag for review
- Create AI\-driven variance analysis tools that automatically generate explanatory narratives for budget vs. actual differences
- Integrate external data sources and alternative data using AI to enhance forecast accuracy
- Develop sentiment analysis capabilities to incorporate qualitative factors into quantitative planning models
- AI Agent \& Solutions Development:
- Design and deploy AI agents using platforms such as Claude Cowork, OpenAI APIs, and Azure AI Services to automate routine planning tasks
- Build custom AI solutions that assist finance users with natural language queries, scenario modeling, and variance analysis
- Develop intelligent automation workflows using AI to streamline data collection, consolidation, and reporting processes
- Create conversational AI interfaces that enable business users to interact with planning data through natural language
- Implement retrieval\-augmented generation (RAG) systems to provide context\-aware financial insights and recommendations
- Train and fine\-tune large language models (LLMs) on Guardian\-specific financial data and terminology
- Governance, Controls, \& Model Integrity
- Conduct rigorous testing and validation of models
- Proactively monitor and improve model performance identifying improvement areas to optimize performance and reliability.
- Drive iterative enhancements, maximizing business value realization and user satisfaction.
- Ensure models adhere to AI governance policies, regulatory standards, and ethical guidelines, including bias mitigation and explainability.
- Implement model risk controls, maintain audit trails, and ensure documentation supports auditability and compliance with evolving regulatory requirements.
- Collaboration \& Enablement:
- Partner with FP\\\&A, Risk, IT, and Data Science teams to identify opportunities for AI\-enhanced planning solutions
- Conduct training sessions and create documentation for finance users on AI\-powered planning tools
- Serve as the subject matter expert on the intersection of EPM and AI technologies
- Evangelize AI capabilities and drive adoption of intelligent planning solutions across the organization
- Mentor team members on AI tools, prompt engineering, and best practices for human\-AI collaboration
You have
Required Qualifications
Technical Skills:
- 5\+ years of hands\-on experience with Oracle EPM Planning (PBCS/EPBCS) including model design, business rules, and integrations
- 2\+ years of experience working with AI/ML technologies, including LLMs, prompt engineering, and AI agent development
- Proficiency in Groovy scripting with demonstrated use of AI coding assistants (Amazon CodeWhisperer, or similar)
- Experience with AI platforms such as Anthropic, OpenAI APIs, Azure OpenAI Service, or similar
- Strong SQL skills and experience with data integration tools (ODI, FDMEE, or similar)
- Familiarity with Python for AI/ML model development and data analysis
- Understanding of machine learning concepts including supervised learning, time series forecasting, and natural language processing
- Experience with cloud platforms (Azure, AWS, or GCP) and their AI services
AI \& Emerging Technology Skills:
- Demonstrated ability to design and implement AI agents and chatbots for business applications
- Experience with prompt engineering and optimization for LLMs
- Knowledge of RAG architectures and vector databases for knowledge retrieval
- Understanding of AI model evaluation metrics and performance monitoring
- Familiarity with AI ethics, bias detection, and responsible AI practices
- Experience with low\-code/no\-code AI platforms for rapid prototyping
Finance \& Business Acumen:
- Strong understanding of FP\&A processes, budgeting, forecasting, and financial modeling
- Experience in financial services or insurance industry preferred
- Ability to translate complex AI capabilities into business value for finance stakeholders
- Understanding of regulatory requirements and controls in financial planning
Soft Skills:
- Excellent communication skills with ability to explain AI concepts to non\-technical audiences
- Strong problem\-solving abilities and creative thinking for AI solution design
- Ability to work independently and manage multiple AI initiatives simultaneously
- Collaborative mindset with experience working in cross\-functional teams
- Adaptability and eagerness to learn emerging AI technologies
What Success Looks Like
- Successful deployment of AI\-enhanced Oracle Planning models that improve forecast accuracy by measurable margins
- Development and adoption of AI agents that reduce manual planning tasks by 30%\+
- Creation of AI solutions that enable self\-service analytics for finance users
- Implementation of AI governance framework that ensures responsible and compliant AI usage
- Positive user feedback and high adoption rates for AI\-powered planning tools
- Demonstrated ROI from AI investments through time savings and improved decision quality
Location
- Hybrid: 3 days a week in office \- NYC, Hudson Yards; or Stamford CT. 2 days WFH
Salary Range:
$118,980\.00 \- $195,465\.00
The salary range reflected above is a good faith estimate of base pay for the primary location of the position. The salary for this position ultimately will be determined based on the education, experience, knowledge, and abilities of the successful candidate. In addition to salary, this role may also be eligible for annual, sales, or other incentive compensation.
Our Promise
At Guardian, you’ll have the support and flexibility to achieve your professional and personal goals. Through skill\-building, leadership development and philanthropic opportunities, we provide opportunities to build communities and grow your career, surrounded by diverse colleagues with high ethical standards.
Inspire Well\-Being
As part of Guardian’s Purpose – to inspire well\-being – we are committed to offering contemporary, supportive, flexible, and inclusive benefits and resources to our colleagues. Explore our company benefits at www.guardianlife.com/careers/corporate/benefits. *Benefits apply to full\-time eligible employees. Interns are not eligible for most Company benefits.*
Equal Employment Opportunity
Guardian is an equal opportunity employer. All qualified applicants will be considered for employment without regard to age, race, color, creed, religion, sex, affectional or sexual orientation, national origin, ancestry, marital status, disability, military or veteran status, or any other classification protected by applicable law.
Accommodations
Guardian is committed to providing access, equal opportunity and reasonable accommodation for individuals with disabilities in employment, its services, programs, and activities. Guardian also provides reasonable accommodations to qualified job applicants (and employees) to accommodate the individual's known limitations related to pregnancy, childbirth, or related medical conditions, unless doing so would create an undue hardship. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment, please contact [email protected]. Please note: this resource is for accommodation requests only. For all other inquires related to your application and careers at Guardian, refer to the Guardian Careers site.
Visa Sponsorship
Guardian Life is not currently or in the foreseeable future sponsoring employment\-based visas (e.g., such as an H\-1B). In order to be a successful applicant, you must be legally authorized to work in the United States, without the need for employer sponsorship/support now or at any time in the future.
Notice Regarding Guardian’s Use of Artificial Intelligence in Recruitment
As part of Guardian’s job application process, Guardian may use artificial intelligence tools (“AI Tools") to automate the sorting and filtering of information provided by applicants as part of its preliminary screening. This preliminary screening may be used to help identify applicant materials and resumes relative to their indication that the applicant meets the requirements for the specific job for which they are applying, as specified in the listing posted on Guardian’s jobs website (Careers at Guardian at https://www.guardianlife.com/careers). At Guardian, we do not use AI Tools to substantially assist or replace human judgment or discretionary decision making in our hiring process. All hiring decisions will be made by Guardian colleagues.
Please be aware that if you apply for a specific position with Guardian, you will have the choice of opting out of Guardian’s use of AI Tools during the job application process. If you would like to request an alternative process that does not utilize AI Tools or would like to request a reasonable accommodation, within ten business days of your position application, you must email your request to [email protected], making sure to provide your name and job requisition identification number. Guardian will retain your applicant materials and resume and all information therefrom in accordance with Guardian’s document retention policy, a copy of which you may request via [email protected].
Additionally, at applicable times, Guardian will make public the most recent bias audit results for such AI tools, which may be found here.
Current Guardian Colleagues: Please apply through the internal Jobs Hub in Workday.
Salary Context
This $118K-$195K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Guardian Life, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($157K) sits 27% below the category median. Disclosed range: $118K to $195K.
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.
Guardian Life AI Hiring
Guardian Life has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $195K - $250K.
Location Context
AI roles in New York pay a median of $220,000 across 1,650 tracked positions.
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 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).
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 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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