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About This Role
Overview:
Provides strategic and technical leadership for the organization's AI, ML, and advanced analytics capabilities. This role defines the vision, roadmap, and standards for AI-driven innovation, leads a team of AI/ML engineers and data scientists, and partners with business and technology leaders to deliver scalable solutions that generate insights, improve decision-making, optimize operations, and create measurable business value. Responsibilities: Essential Functions The list of essential functions, as outlined herein, is intended to be representative of the duties and responsibilities performed within this classification. It is not necessarily descriptive of any one position in this class. The omission of an essential function does not preclude management from assigning duties not listed herein if such functions are a logical assignment to the position.
- Provides strategic leadership for the organization's AI, machine learning, and advanced analytics capabilities and establishes the vision, roadmap, standards, and operating model for AI-driven innovation
- Directs the design, development, deployment, and lifecycle management of AI solutions, machine learning models, intelligent agents, and advanced analytics applications that address complex business challenges
- Oversees the development of generative AI capabilities, including Retrieval-Augmented Generation (RAG), foundation model customization, vector search technologies, embeddings, and enterprise knowledge integration
- Establishes enterprise standards for agent orchestration, model evaluation, MLOps, monitoring, governance, and responsible AI practices that ensure scalable and reliable solutions
- Leads the application of advanced statistical methods, predictive modeling, experimentation, forecasting, and analytical techniques that support strategic decision-making and operational improvement
- Partners with business, product, and technology leaders and aligns AI/ML priorities, capabilities, and delivery roadmaps with organizational objectives and measurable business outcomes
- Directs the development of AI-ready data assets, feature engineering capabilities, and model training frameworks that support machine learning and generative AI initiatives
- Establishes and enforces governance, security, compliance, documentation, and quality standards that promote transparency, reproducibility, and responsible AI adoption
- Drives performance management and optimization activities across AI models, platforms, and operational processes to maximize accuracy, efficiency, business value, and cost effectiveness
- Manages AI/ML infrastructure, cloud services, vendor relationships, budgets, and resource planning to ensure secure, scalable, and efficient operations
- Builds, leads, and develops a high-performing team of AI/ML engineers, data scientists, and analytics professionals through coaching, mentoring, talent development, and performance management
- Evaluates emerging technologies, industry trends, and market developments and advances the organization's AI, machine learning, and advanced analytics capabilities.
Qualifications:
To perform this job successfully, an individual must be able to perform each essential duty and responsibility satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required.
EDUCATION AND/OR EXPERIENCE:
Bachelor’s degree (preferred Masters of Science) in Computer Science, Analytics, or related field from an accredited college or university. Ten (10) years of progressive experience in data engineering, data science, or a related role, with hands-on experience building and deploying machine learning models, including three (3) or more years in a technical leadership or management capacity leading AI/ML teams and delivering enterprise-scale initiatives.
CERTIFICATES, LICENSES, REGISTRATIONS AND DESIGNATIONS:
None
KNOWLEDGE, SKILLS AND ABILITIES
- Advanced knowledge in Python, SQL, machine learning frameworks, and modern analytics technologies used for model development, deployment, and optimization
- Advanced knowledge of generative AI technologies, including AI agents, multi-agent systems, prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation architectures
- Advanced knowledge in supervised and unsupervised machine learning techniques, model evaluation, feature engineering, and performance measurement methodologies
- Proficient in cloud-based AI and analytics platforms, data architectures, data warehousing, and ETL/ELT processes
- Strong knowledge of model customization approaches, including supervised fine-tuning, retrieval-augmented fine-tuning, and parameter-efficient training methods
- Proficient in MLOps, model monitoring, lifecycle management, observability, governance, and responsible AI practices
- Proficient in establishing engineering best practices, including version control, code reviews, testing, CI/CD, and collaborative development standards
- Advanced statistical knowledge in hypothesis testing, experimental design, forecasting, causal inference, regression analysis, and uncertainty quantification
- Demonstrates strong leadership, strategic planning, organizational development, and change management capabilities
- Ability to communicate, engage stakeholders, and demonstrate executive presentation skills and translate complex technical concepts into business value
- Ability to lead enterprise AI initiatives, managing budgets and vendors, and delivering measurable business outcomes through AI, machine learning, and advanced analytics solutions.
PHYSICAL REQUIREMENTS: The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Functions involve the ability to exert light physical effort usually involving some lifting, carrying, pushing and/or pulling of objects and materials of light weight (up to 20 pounds). May involve some climbing, balancing, stooping, kneeling, crouching, crawling, walking or standing.
ENVIRONMENTAL REQUIREMENTS: The work environment characteristics described here are representative of those an employee may encounter while performing the essential functions of this job.
Functions are regularly performed inside without potential for exposure to adverse conditions, such as inclement weather, atmospheric elements and pathogenic substances. The noise level in the work environment is usually moderate.
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 OneBlood, 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 $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.
OneBlood AI Hiring
OneBlood has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Saint Petersburg, 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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