Interested in this AI/ML Engineer role at BST Global?
Apply Now →About This Role
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Department:
- Product Management
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Location:
- Tampa, FL
Summary of Duties & Responsibilities
The Product Director of AIA is a key leadership role in the Product Management Group. The Product Director is responsible for establishing the product direction and vision for the AIA product area and is aligned with the BST Global corporate goals and strategies. This role is responsible for providing leadership and oversight for all activities being executed by their cross-functional product team as it relates to all phases of the product delivery lifecycle. Efforts include go-to-market planning in collaboration with Sales, Solutions, Support, Marketing, and Client Management and continue through product development, quality assurance and release readiness processes. The Product Director is considered a thought leader and subject matter expert, which includes responding to market trends, presenting at key events, and writing blogs, white papers and articles as needed.
Essential Functions
- Lead and participate with their product manager in establishing the vision, strategy and tactical plan for their product line
- Provide industry expertise in all aspects of analytics, big data and the knowledge management market vertical, including market analysis, best-in-practice business processes, and technology trends currently being leveraged in the space as well as the future direction for the platform and integration product area
- Lead product management activities and report to the product manager
- Understand competitive products and landscape, crafting their production vision and roadmap accordingly
- Work directly with the product/discipline directors to ensure compliance with process and procedures outlined in the SDLC and proper priority in the evolution of their products and solutions
- Collaborate with and support Sales, Marketing, and Client Management in sales and client relationship activities related to their product area and expertise
- Engage with the Solutions and Support groups to collaborate and/or assist with implementation and product issues raised during implementations or in production support and ensure all are resolved
- Monitor and collaborate on significant engagements related to the product area to stay abreast of feature application and gain direct client feedback/input for solution enhancement/evolution
- Resolve internal and client-facing issues/conflicts
- Provide BST Global user conference content and participation
- Promote the product area by participating in speaking and publishing activities
- Brief BST Global executive leadership and fellow operations leaders on the scope, assumptions, and risks related to the delivery of their product initiatives and vision
Skills & Competencies
- Leadership – Effectively enlist the aid and support of their Product team and others in achieving corporate goals and objectives
- Proactively participates in corporate leadership forums, taking ownership of corporate direction and outcomes
- Knowledge – Possess and evolve adequate and relevant knowledge specific to their product area and related function
- Establish and evolve appropriate product management processes, procedures and supporting systems
- Product Vision, Strategy & Tactical Execution – Establish product vision in harmony with and support of the corporate vision, evolve product strategy consistent with product vision, and establish and execute a tactical plan to deliver on strategy
- Resource Management – Own and progress the Product team’s organizational structure.
- Hire, mentor, complete performance appraisals, train and advise on career development.
- Promote the corporate culture and staff morale
- Collaboration – Work effectively and proactively with others to achieve shared goals and objectives
- Judgment – Consistently execute timely and reasoned judgment in concert with appropriate and considered input from others
Education or Prior Work Experience
- Five or more years of general ledger accounting experience required
- Strong understanding of core accounting principles and financial processes required
- Bachelor’s degree in accounting, finance, business, information systems or related field preferred
- Experience implementing, supporting, facilitating and/or testing financial accounting systems preferred
- Experience working with ERP platforms a plus
- Experience with project-based accounting or professional services environments a plus
Team Support/ Leadership
Deep commitment to working in and fostering a highly collaborative, innovative and high-performing team
- Promote, support, and embody BST Global’s company mission, strategy and values
- Foster a culture of ownership and pride in delivering the highest levels of quality and excellence
Education or Prior Work Experience
- Bachelor’s degree in a relevant area
- Advanced degree beneficial but not required
- Preferred areas of academic focus include MIS, IT management and business management
- Requires 10 or more years of experience relevant to the P&L in product management, specific relevant professional service industry experience that includes strategic and tactical leadership responsibilities
- Technical understanding of big data architectural patterns, machine learning algorithms and large language models
- Preferred experience in data platforms such as DataBricks and Microsoft Fabric
Reports To
Chief Product Officer
Number Supervised
0-3
Travel
Up to 25%
Classification
Exempt
Work Environment & Physical Demands
This job operates in a professional office environment. This role routinely uses standard office equipment such as computers, phones, photocopiers, filing cabinets and fax machines.
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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At BST Global, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
BST Global AI Hiring
BST Global has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Tampa, FL, US.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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