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About This Role
Position Summary
The AI Solutions Engineer will play a crucial role in delivering AI\-powered automation solutions for our Clients and internal operations. The AI Solutions Engineer will be responsible for designing, building, and maintaining AI applications, whether starting new projects from the ground up or carrying in\-progress builds through development, testing, and production release, then supporting ongoing refinements following go\-live. This is a true hybrid role. The right candidate configures and integrates AI tools and LLM platforms, and is equally comfortable interviewing stakeholders, documenting workflows, and translating business needs into technical specifications. In this role, the ability to learn quickly, take ownership, attention to detail, and pride in your work are crucial to our and your continued success.
Duties/Responsibilities
- AI Development: Design, build, and maintain AI\-powered applications and automations using LLM platforms and APIs (OpenAI, Claude, etc.), Azure services, and low\-code tools, including prompt engineering, testing, and evaluation of model outputs.
- Project Ownership: Own projects from initial concept through production release while meeting or exceeding defined accuracy and performance targets.
- Systems Integration: Configure and maintain integrations between AI tools, business systems, and third\-party platforms to support automated workflows.
- Requirements Gathering: Conduct stakeholder interviews, map business processes, document requirements, and translate business needs into technical specifications and user stories.
- Testing and Quality Assurance: Plan and coordinate user acceptance testing, monitor production accuracy and performance following go\-live, and identify and implement refinements.
- Documentation: Create and maintain technical documentation, process maps, and end\-user guidance for both internal teams and Clients.
- Client Delivery: Support billable Client engagements including AI development, business analysis, and reporting work.
- Communication: Communicate effectively with the development team, management, and Clients to provide clear status updates, surface risks early, and guarantee prompt and responsive delivery.
The successful candidate will be a self\-starter who learns quickly, takes ownership, and is comfortable working on both new builds and work in progress. They will have a passion for excellence, strong problem\-solving instincts, and the ability to work independently or in a group setting. Because this role bridges technical and business audiences, the candidate must communicate clearly and professionally in both verbal and written formats.
Education and Experience
The ideal candidate will possess the following abilities, attributes, experience and skills:
- Bachelor's Degree in AI, Data Analytics, Computer Science, Information Systems, or a related field is preferred; equivalent practical experience will be considered.
- 1\-5 years of relevant experience, including internships; aptitude and learning ability are prioritized over years of experience.
- Hands\-on experience with AI tools and LLM platforms such as OpenAI or Claude, including prompt engineering and output evaluation (required).
- Understanding of Azure services or comparable cloud platforms (required).
- Understanding of APIs and how systems connect and exchange data (required).
- Business analysis experience including requirements gathering, stakeholder interviews, process mapping, or user story writing is highly preferred.
- Demonstrated ability to own a project from concept through completion is highly preferred.
- Exposure to scripting or programming languages such as Python is desired but not required.
- Exposure to Power BI, SQL, or data analysis tools is desired but not required.
- Familiarity with PSA or ticketing platforms is desired but not required.
- Azure certifications (AZ\-900 or AI\-900\) are desired but not required; AI\-102 is a plus.
- Ability to work under pressure, manage multiple priorities, and interpret instructions furnished in written, oral, diagram, or schedule form.
- Solid decision\-making, problem\-solving approaches, and ability to support routine to complex analysis efficiently and accurately.
Why Join Bridgehead IT?
Serve as a trusted advisor for a diverse portfolio of SMB and Major Accounts.
Influence technology strategy and business outcomes.
Work alongside highly skilled engineers, architects, and technology leaders.
Gain exposure to a wide range of industries and technologies.
Enjoy a high\-impact role with significant visibility and growth opportunities.
Be part of a team that values ownership, innovation, accountability, and exceptional client service.
At Bridgehead IT, we believe technology should drive business success. If you're passionate about both technology and people, we'd love to hear from you.
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 Bridgehead IT, 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.
Bridgehead IT AI Hiring
Bridgehead IT has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Antonio, 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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