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About This Role
About The Company
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Built on a foundation of integrity, respect, and commitment, JPI stands out as one of the most active real estate developers and builders in the nation. For more than 35 years, JPI has designed and developed communities with best\-in\-class homes and amenities. JPI is committed to data\-driven management and continuous improvement. Its team of experts utilizes data – including unparalleled market research, proven business processes, and proprietary models – to ensure that every decision is strategic, focused, and produces exceptional multifamily communities with the best risk\-adjusted returns. More than just great communities, JPI is focused on leaving a lasting impact on the communities where we build and is continually committed to “Building What Matters.” JPI operates as a subsidiary of Sumitomo Forestry, which is recognized as one of the largest home builders in the United States.
*Why work for us?*
We have a 35\-year track record of developing multifamily communities with responsibility, accountability, and integrity. Our stated purpose is to:
- Transform Building
- Enhance Communities
- Improve Lives
JPI has an ambitious and exciting vision for how we will achieve this, which makes for a positive and dynamic work environment, with many opportunities for personal development and growth. As well as our highly competitive offering of compensation and benefits, we are committed to:
- Transformative careers in a transformative company
- Comprehensive training and development
- Promotion from within at all levels of the organization
- Borderless Careers, based on performance, potential, and personal ambition
*Industry Recognition*
- NMHC \- \#1 Fastest Growing Developer; \#2 Fastest Growing Building; \#8 Largest National Developer; \# 11 Largest National Builder
- Real Page – Most active multifamily developer in DFW for the past 8 years
- Dallas Business Journal Best Places to Work – 2023
- Dallas Business Journal – Largest DFW Real Estate Developers \- \#11
JPI offers associates a comprehensive benefits package with competitive salaries and more, including:
- Competitive Bonus Program
- 4 Weeks PTO for All New Associates (Pro\-Rated by Hire Date)
- 11 Holidays and 8 Early Release Days
- Medical, Dental, Vision, and Life Insurance
- 401(k) with Company Match (Up to 5% Match)
- Health Savings Account
- Flexible Spending Accounts (Dependent \& Medical Reimbursement)
- Paid Parental Leave
- Paid Volunteer Time
- Tuition Assistance
- Phone Reimbursement
- Associate Referral Bonuses
About the Job.
JPI has an exciting opportunity for an Agentic AI Developer to join the Applications and Integrations team within the Digital \& IT organization in Dallas, TX.
As JPI continues to expand and modernize its AI and software development ecosystem, this role will play a key part in advancing our digital strategy and transforming building. You’ll have a unique opportunity to design, deliver, and lead practical agentic systems, AI\-driven applications, RPA solutions, integrations, and automations that improve business processes and create meaningful impact across the company. The role has a focus on implementing and continually improving AI security, responsible guardrails, and monitoring practices to support safe and reliable adoption.
The Agentic AI Developer will deliver proprietary JPI agents, applications, and system integrations using platforms such as Workato, Oracle Cloud Infrastructure and complementary technologies. This role collaborates closely with technical and non\-technical teams to architect and implement high value process improvements throughout the organization.Essential Functions \& Responsibilities
Agentic AI Development
- Design, develop, test, and maintain agentic AI applications, AI\-powered automations, and RPA solutions that improve business processes across JPI.
- Implement security controls, policy\-based guardrails, access controls, and monitoring capabilities to support the secure and responsible operation of AI agents.
- Validate AI agent behavior through testing, prompt evaluation, logging, and monitoring to improve reliability, accuracy, and compliance.
- Troubleshoot, optimize, and enhance existing AI agents based on operational performance, user feedback, and evolving business requirements.
- Collaborate with senior AI developers to implement enterprise AI standards and security best practices.
Back\-End \& Integrations Development
- Develop and support integrations using Workato, Python, Oracle Cloud, REST APIs, and related technologies.
- Configure secure connections between enterprise applications and third\-party platforms.
- Create and maintain technical documentation for integration workflows, APIs, and agent configurations.
- Monitor the performance of AI agents and system integrations, identifying opportunities to improve scalability, reliability, security, and overall performance.
Solution Architecture \& Development
- Contribute to the development of secure, scalable AI and integration solutions following established architectural standards.
- Translate business requirements into technical specifications and development tasks.
- Build new functionality and enhance existing applications while following secure coding and AI governance practices.
- Participate in testing, validation, and deployment activities to ensure successful production releases.
Agile Sprint Methodology
- Participate in Agile ceremonies including sprint planning, stand\-ups, retrospectives, and backlog refinement.
- Collaborate with cross\-functional teams to deliver user stories and AI capabilities on schedule.
- Participate in peer code reviews and incorporate feedback to improve code quality and maintainability.
- Continuously expand knowledge of agentic AI frameworks, AI security, and emerging technologies.
Collaboration \& Strategy
- Partner with business stakeholders to understand automation opportunities and translate requirements into AI\-enabled solutions.
- Support adoption of agentic AI solutions by assisting with user training, documentation, and knowledge sharing.
- Work with UI developers, architects, and integration engineers to ensure AI solutions align with enterprise standards.
- Promote responsible AI practices by helping implement governance, security, and compliance requirements across AI solutions.
Non\-essential Functions \& Responsibilities
- Other duties as assigned.
Education, Work Experience, \& Physical Requirements
Required Qualifications
- 3\+ years of professional software development experience.
- 1\+ year of Agentic AI development experience.
- 1\+ year implementing AI security controls, safety guardrails, access boundaries, validation checks, and monitoring practices for AI or agentic systems.
- 1\+ year experience with RAG and Vector Stores/Databases.
- 1\+ years of experience with Python, Node.js and/or React.
- 1\+ years of experience working with cloud platforms or iPaaS tools.
- 1\+ years of experience integrating with REST APIs.
- Strong understanding of integration patterns and architecture.
- Excellent problem\-solving skills and a proactive, collaborative mindset.
- Comfortable working in Agile environments.
Preferred Qualifications
- Experience with any of LangGraph, LangGraph, MCP, RPA and Agent Harness
- Experience with Workato.
- Familiarity with any of Workday, BuildingConnected, Procore, Airtable, Oracle Primavera Cloud, Oracle Cloud Infrastructure, Fabric, Power Platform, and Azure.
- Experience with SQL databases
- Experience with GitLab
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 JPI, 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.
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.
JPI AI Hiring
JPI has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
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/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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