Interested in this AI/ML Engineer role at Beehyv?
Apply Now →Skills & Technologies
About This Role
About BeeHyv
BeeHyv is a product engineering and AI company that helps enterprises and high\-growth technology companies build, modernize, and scale software products. For nearly two decades, we've partnered with startups, unicorns, and Fortune 500 companies to solve complex engineering challenges across enterprise software, cloud platforms, data engineering, and digital transformation.
Today, AI is fundamentally changing how software is built and how businesses operate. At BeeHyv, we've invested heavily in enterprise\-ready AI platforms, agentic AI frameworks, and reusable engineering accelerators that enable our customers to modernize legacy systems, accelerate product development, automate business processes, and build intelligent software solutions.
About the Role
We are looking for a Customer Solutions Architect – AI \& Product Engineering to serve as the technical face of BeeHyv for our customers.
This is not a traditional Solutions Architect, Technical Lead, or Presales Engineer role. It is a hands\-on engineering leadership role for someone who enjoys solving complex customer problems while remaining deeply involved in architecture, coding, AI, and delivery.
You will work directly with customer stakeholders to understand their business challenges, product vision, engineering landscape, and technology strategy. You'll transform ambiguous problems into practical solutions, architect modern software systems, and evaluate every customer engagement through an AI lens.
You'll help customers answer questions such as:
- How can AI improve our products?
- Which engineering workflows can be accelerated using AI?
- Can intelligent agents automate parts of our business?
- How can we modernize our platform while making it AI\-ready?
- How can BeeHyv's AI frameworks accelerate our implementation?
You'll remain hands\-on throughout the engagement—building prototypes, reviewing code, solving difficult technical problems, mentoring engineers, and guiding delivery through BeeHyv's engineering teams.
Why This Role Matters
The software industry is entering an AI\-first era.
Customers no longer need partners who can simply build software. They need partners who can help them understand where AI creates real business value, distinguish genuine opportunities from hype, and successfully deploy AI into production.
This role sits at the intersection of Customer Engagement, Product Engineering, AI Strategy, Enterprise Architecture, Hands\-on Software Engineering, and Delivery Leadership.
Every customer engagement should leave the customer with not only better software, but a clearer AI strategy.
Key Responsibilities
Customer Discovery \& AI Opportunity Identification
- Work directly with customer engineering leaders, product managers, architects, and business stakeholders to understand business goals, workflows, technical landscape, and operational challenges.
- Discover the real problem behind the stated requirement rather than simply gathering requirements.
- Evaluate every engagement through an AI lens, identifying opportunities where AI, intelligent automation, or agentic workflows can create measurable business value.
- Analyze applications, APIs, integrations, and data flows to identify modernization opportunities.
- Build trusted relationships with customers and become their technical advisor.
Solution Architecture \& Hands\-on Engineering
- Architect scalable enterprise software solutions spanning applications, integrations, cloud platforms, APIs, data engineering, and AI\-enabled systems.
- Build rapid prototypes and proof\-of\-concepts to validate architecture and AI use cases.
- Remain hands\-on by contributing code, reviewing implementations, debugging complex production issues, and mentoring engineering teams.
- Design solutions that balance scalability, maintainability, security, delivery velocity, and business outcomes.
- Clearly communicate architectural trade\-offs to both technical and executive stakeholders.
AI Platform Leadership
- Become an expert in BeeHyv's AI platforms and engineering accelerators, including our Agentic AI Framework, Genie Enterprise Knowledge Platform, Talk2API, and other reusable AI assets.
- Confidently demonstrate and position BeeHyv's AI capabilities during customer discussions, workshops, and solution presentations.
- Design enterprise AI solutions using LLMs, RAG, intelligent agents, workflow orchestration, AI evaluation, and enterprise guardrails.
- Apply AI throughout the software development lifecycle to improve engineering productivity, software quality, and delivery speed.
- Continuously identify reusable patterns, connectors, prompts, agents, evaluation techniques, and implementation approaches that strengthen BeeHyv's AI platforms.
- Work closely with BeeHyv's AI engineering teams to evolve our AI frameworks based on real customer engagements.
- Stay current with advances in AI and continuously evaluate how emerging technologies can benefit BeeHyv and our customers.
Delivery Leadership
- Lead technical execution through BeeHyv's engineering teams.
- Break complex initiatives into practical implementation plans.
- Provide technical direction throughout the delivery lifecycle.
- Remove ambiguity, mentor engineers, review designs, and maintain engineering excellence.
- Take ownership from discovery through successful production deployment.
Customer Success \& Business Growth
- Support discovery workshops, architecture sessions, technical proposals, and proof\-of\-concept engagements.
- Contribute to solution estimates, Statements of Work, and technical proposals.
- Identify opportunities to expand customer engagements through AI adoption, modernization, cloud engineering, data platforms, or product development.
- Represent BeeHyv's engineering and AI capabilities with credibility and confidence.
What We're Looking For
We're looking for engineers who have embraced AI as a fundamental part of how they build software.
You should be equally comfortable discussing architecture with a CTO, designing an AI workflow on a whiteboard, building a proof\-of\-concept, reviewing Java code with engineers, debugging a production issue, or mentoring a distributed engineering team.
You should thrive in ambiguity, remain deeply hands\-on, and naturally take ownership.
Most importantly, you should understand that AI is not simply another technology stack—it is transforming how software is designed, built, tested, deployed, and operated. We are looking for someone who wants to help customers navigate that transformation.
Required Qualifications
- 8\+ years of software engineering experience with strong hands\-on expertise in Java and enterprise application development.
- Experience designing and building scalable enterprise applications, distributed systems, APIs, integrations, and cloud\-native architectures.
- Demonstrated ability to remain hands\-on while leading customer engagements and engineering teams.
- Experience working directly with enterprise customers in a technical leadership, architecture, or consulting role.
- Strong solution architecture and system design skills.
- Experience building prototypes, solving complex technical problems, and contributing production\-quality code.
- Hands\-on experience using AI\-assisted software development tools as part of your daily engineering workflow.
- Experience designing or building AI\-enabled applications, or a strong understanding of modern AI application architectures including LLMs, RAG, intelligent agents, workflow orchestration, and AI evaluation.
- Excellent communication and stakeholder management skills.
Preferred Qualifications
- Experience in product engineering services or technology consulting.
- Experience building enterprise AI applications or AI\-powered products.
- Experience with AWS, Azure, or Google Cloud Platform.
- Experience with Kubernetes, Docker, and modern DevOps practices.
- Experience supporting presales, technical workshops, and customer discovery sessions.
- Exposure to product management or product strategy.
Why Join BeeHyv
You'll work at the intersection of enterprise software engineering and applied AI, partnering with startups, high\-growth technology companies, and global enterprises to solve some of their most important engineering challenges.
You'll influence technology strategy, architect modern software platforms, lead customer engagements, remain hands\-on with engineering, and help shape BeeHyv's AI platforms through real\-world customer implementations.
If you enjoy solving difficult problems, building software, working directly with customers, and helping define how AI is applied in enterprise software, we'd love to talk to you.
Benefits:
- Health insurance
- Paid time off
Work Location: In person
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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Beehyv, 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Beehyv AI Hiring
Beehyv has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in McKinney, TX, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.