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About This Role
\#WeAreTradeStation
Remote Position \- must reside Florida, Texas, Illinois, New York, New Jersey, Alabama, Arizona, Arkansas, Colorado, Connecticut, Delaware, Georgia, Indiana, Kansas, Massachusetts, Missouri, North Carolina, Tennessee, Utah or Wisconsin
Who We Are:
TradeStation is the home of those born to trade. As an online brokerage firm and trading ecosystem, we are focused on delivering the ultimate trading experience for active traders and institutions. We continuously push the boundaries of what's possible, encourage out\-of\-the\-box thinking, and relentlessly search for like\-minded innovators.
At TradeStation, we are building an AI\-First culture. We expect team members to embrace AI as a core part of their daily workflow, whether that’s using AI to accelerate development, enhance decision\-making, improve client outcomes, or streamline internal processes. We hire, grow, and promote people who can harness AI responsibly and creatively. We treat AI as a partner in problem\-solving, not just a tool; following our governance standards to ensure AI is used ethically, securely, and transparently. If you join us, you’re joining a culture where *AI is how we wor*k.
Are you ready to make yourself at home?
What We Are Looking For:
We are looking for a Principal AI Solutions Engineer who will be responsible for designing, implementing, and optimizing AI/LLM solutions that drive business value across Brokerage Services Dev. This role requires deep hands\-on expertise in AI/ML systems, strong engineering fundamentals, and the ability to bridge technical implementation with business requirements. This role will work closely with the VP, AI Innovation and Transformation to architect and build production AI systems, collaborate closely with business stakeholders to understand requirements, and establish technical standards for AI/LLM deployment.
What You’ll Be Doing:
Data Platform \& BI Integration
- Help develop and maintain data models, SQL queries, and analytics workflows in Databricks
- Support BI reporting infrastructure including Power BI and Sigma integrations
- Implement data quality monitoring, anomaly detection, and automated alerting systems
- Partner with EA/Platform teams on data pipeline development and optimization
Technical Architecture \& Platform Development
- Architect scalable AI solutions leveraging Databricks, Unity Catalog, and modern data platforms
- Help design and implement data pipelines, feature engineering workflows, and ML infrastructure
- Establish technical patterns and best practices for AI/LLM system development
- Build tooling and frameworks that accelerate AI solution delivery across teams
AI/LLM Solution Engineering
- Design and implement production\-grade AI/LLM systems including RAG pipelines, prompt engineering frameworks, and evaluation workflows
- Build and optimize MCP integrations, AI agent architectures, and LLM orchestration patterns
- Develop guardrails, observability systems, and monitoring solutions for AI/LLM applications
- Work hands\-on with model deployment, fine\-tuning, and performance optimization
Business Requirements \& Solution Design
- Partner with business stakeholders to translate requirements into technical solutions
- Conduct technical discovery, assess feasibility, and define solution architectures
- Create technical specifications, design documents, and implementation plans
- Collaborate with Data Science and ML Engineering teams on model development and deployment
Operational Excellence
- Establish observability and monitoring for production AI systems
- Implement cost tracking and optimization strategies for compute and serverless resources
- Build experimentation frameworks (A/B testing, pilots) and evaluation methodologies
- Drive continuous improvement through performance analysis and system optimization
Governance \& Risk Management
- Implement responsible AI practices including safety, fairness, and privacy controls
- Develop model risk management processes and documentation
- Establish access governance patterns for Databricks resources and AI platforms
- Create technical documentation, runbooks, and knowledge\-sharing materials
The Skills You Bring:
- Strong software engineering fundamentals with experience building production systems
- Deep technical expertise in AI/LLM technologies, including prompt engineering, RAG systems, and agent frameworks
- Hands\-on experience with Databricks platform (SQL Warehouses, Unity Catalog, MLflow) and data engineering
- Proficiency in Python, SQL, and modern ML/AI frameworks and libraries
- Experience with cloud platforms and infrastructure as code
- Strong understanding of data modeling, pipeline development, and analytics workflows
- Familiarity with BI tools (Power BI, Sigma) and data visualization
- Experience with Agile development practices and tools (Git, Jira, CI/CD)
- Knowledge of experimentation methodologies, A/B testing, and statistical analysis
- Understanding of responsible AI principles, model risk management, and governance
- Excellent communication skills with ability to explain technical concepts to business stakeholders
- Ability to prioritize competing demands, maintain focus on critical path items, and drive projects from conception to production deployment
- Strong problem\-solving ability and experience working in fast\-paced environments
- Proven track record of building and deploying production AI/LLM applications
- Strong hands\-on experience with Databricks, modern data platforms, and cloud infrastructure
- Demonstrated ability to work across business and technical stakeholders to deliver impactful solutions
- Deep hands\-on experience with modern data platforms including data lakes, Delta Lake, Unity Catalog, and Lakehouse architectures preferred
- Proven track record building and scaling RAG systems in production environments preferred
- Experience implementing Model Context Protocol (MCP) servers and integrations preferred
- Experience with prompt engineering frameworks, evaluation systems, and LLM observability tools preferred
- Familiarity with AI governance frameworks and responsible AI implementation in enterprise settings preferred
- Published work, open\-source contributions, or conference presentations related to AI/ML systems preferred
- Experience with real\-time data processing and stream processing frameworks (Kafka, Spark Streaming) preferred
- Knowledge of cost optimization strategies for cloud\-based ML workloads and serverless architectures preferred
Minimum Qualifications:
- 4\+ years of experience in software engineering, ML engineering, data engineering, or related technical roles with significant focus on AI/ML systems
- Bachelor's degree in Computer Science, Engineering, Data Science, or related technical field; equivalent experience considered
Desired Qualifications:
- 7\+ years of experience in software engineering, ML engineering, or data engineering with at least 3 years focused on production AI/LLM systems
- Master's degree or PhD in Computer Science, Machine Learning, Data Science, or related technical field
- Databricks Certified Machine Learning Professional or Data Engineer Professional certification
- Cloud platform certification (AWS Solutions Architect, Azure AI Engineer, or Google Cloud Professional Machine Learning Engineer)
What We Offer:
- Collaborative work environment
- Competitive Salaries
- Yearly bonus
- Comprehensive benefits for you and your family starting Day 1
- Unlimited Paid Time Off
- Flexible working environment
- TradeStation Account employee benefits, as well as full access to trading education materials
- Pay Range (US) $160\-175K (Countries outside of the US have differing ranges in accordance with local labor markets)
*TradeStation provides equal employment opportunities to current and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, sexual orientation, age, pregnancy, disability, handicap, citizenship, veteran or marital status, or any other legally recognized status entitled to protection under federal, state, or local anti\-discrimination laws.*
\#LI\-Remote
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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At TRADESTATION, 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 $181,170 based on 12,692 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 $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
TRADESTATION AI Hiring
TRADESTATION has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.
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