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About This Role
It's fun to work in a company where people truly BELIEVE in what they are doing!
*We're committed to bringing passion and customer focus to the business.*
Fractal is a strategic AI partner to Fortune 500 companies with a vision to power every human decision in the enterprise. Fractal is building a world where individual choices, freedom, and diversity are the greatest assets; an ecosystem where human imagination is at the heart of every decision. Where no possibility is written off, only challenged to get better. We believe that a true Fractalite is the one who empowers imagination with intelligence. Fractal has been featured as a Great Place to Work by The Economic Times in partnership with the Great Place to Work® Institute and recognized as a ‘Cool Vendor’ and a ‘Vendor to Watch’ by Gartner.
Please visit Fractal \| Intelligence for Imagination for more information about Fractal.
Job Title: Senior Agentic AI Engineer / AI\-ML Engineer
Location
Remote
Experience
5–10\+ Years
About the Role
We are seeking a highly skilled Agentic AI Engineer to design and build intelligent automation solutions for large\-scale enrollment and benefits administration systems. The platform currently processes over 15 million enrollment\-related transactions, with approximately 80% of scenarios handled through deterministic rule\-based automation and the remaining 20% requiring manual intervention.
The objective of this role is to leverage Agentic AI, LLMs, and machine learning techniques to automate complex exception handling, accelerate rule creation, reduce testing effort, and continuously evolve the decisioning framework as new scenarios emerge in production.
The ideal candidate will have strong expertise across AI/ML engineering, full\-stack development, MLOps, workflow orchestration, and rule\-engine modernization.
Key Responsibilities
Agentic AI \& Intelligent Automation
- Design and develop Agentic AI solutions to automate complex enrollment change scenarios currently requiring manual intervention.
- Build AI\-powered agents capable of analyzing exceptions, recommending actions, and creating new rule definitions from emerging production scenarios.
- Develop scalable orchestration frameworks using LangGraph, LangChain, and modern AI agent architectures.
Rule Engine Modernization
- Work on next\-generation rule\-based systems that combine deterministic business rules with probabilistic AI reasoning.
- Convert and operationalize business rules represented in DMN/XML formats using tools such as PyDMN.
- Enable continuous learning mechanisms where new production cases can be analyzed and translated into new business rules.
Test Automation \& Scenario Generation
- Develop AI\-assisted test case generation frameworks for highly complex rule environments containing hundreds of business scenarios and edge cases.
- Identify unseen exception paths and generate test scenarios that may not have been anticipated by SMEs.
- Reduce testing cycles and accelerate deployment through intelligent validation and simulation.
Machine Learning \& Data Science
- Build models and decision\-support systems for scenario classification, exception handling, and rule recommendation.
- Evaluate the stability, reliability, and performance of LLM\-driven workflows.
- Design feedback loops for continuous improvement of AI\-generated recommendations.
MLOps \& Productionization
- Implement scalable MLOps pipelines for model deployment, monitoring, and governance.
- Build observability and evaluation frameworks for AI agents and rule engines.
- Ensure reliability, scalability, and compliance in production environments processing millions of transactions.
Required Skills
AI \& Machine Learning
- Strong experience in Machine Learning, Generative AI, and Agentic AI systems.
- Experience building AI agents using:
+ LangGraph
+ LangChain
+ OpenAI / Anthropic / Azure OpenAI ecosystems
- Knowledge of probabilistic reasoning and AI\-driven decision systems.
Programming \& Engineering
- Expert\-level Python development skills.
- Experience building production\-grade applications and APIs.
- Strong software engineering practices including testing, CI/CD, and code quality.
Rule Engines \& Decision Systems
- Experience with business rule engines and decision management platforms.
- Understanding of DMN (Decision Model and Notation) and XML\-based rule representations.
- Experience with PyDMN or similar decision automation frameworks preferred.
MLOps \& Cloud
- Experience with model deployment, monitoring, and lifecycle management.
- Knowledge of containerization and cloud\-native architectures.
- Familiarity with ML observability and evaluation frameworks.
Data Science
- Strong analytical and problem\-solving skills.
- Experience working with large\-scale transactional systems and operational data.
Preferred Qualifications
- Experience in healthcare, insurance, benefits administration, or enrollment platforms.
- Experience processing high\-volume transactional workloads (10M\+ transactions).
- Knowledge of decision intelligence and business process automation.
- Experience implementing AI\-assisted software testing frameworks.
- Familiarity with hybrid deterministic \+ LLM\-based decision systems.
Success Metrics
- Reduction in manual processing for enrollment change requests.
- Increased automation coverage beyond current rule\-based capabilities.
- Faster rule creation and deployment for new business scenarios.
- Significant reduction in testing and development cycles through AI\-generated test scenarios.
- Improved handling of edge cases and previously unseen production exceptions.
- Scalable deployment of Agentic AI solutions across 120\+ business scenarios.
Pay:
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Fractal, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is: $120,000 to $140,000 Yearly. In addition, you may be eligible for a discretionary bonus for the current performance period.
Benefits:
As a full\-time employee of the company or as an hourly employee working more than 30 hours per week, you will be eligible to participate in the health, dental, vision, life insurance, and disability plan in accordance with the plan documents, which may be amended from time to time. You will be eligible for benefits on the first day of employment with the Company. In addition, you are eligible to participate in the Company 401(k) Plan after 30 days of employment, in accordance with the applicable plan terms. The Company provides 11 paid holidays and 12 weeks of Parental Leave. We also follow a “free time” PTO policy, allowing you the flexibility to take the time needed for either sick time or vacation.
*Fractal provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.*
If you like wild growth and working with happy, enthusiastic over\-achievers, you'll enjoy your career with us!
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Salary Context
This $120K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Fractal Analytics, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($130K) sits 41% below the category median. Disclosed range: $120K to $140K.
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.
Fractal Analytics AI Hiring
Fractal Analytics has 4 open AI roles right now. They're hiring across Data Scientist, MLOps Engineer, AI/ML Engineer. Positions span CA, US, New York, NY, US. Compensation range: $140K - $205K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 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 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).
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 3,708 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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