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About This Role
Overview:
Oversees the coding, pipeline development, execution, and delivery of Artificial Intelligence (AI) and Machine Learning (ML) projects across the organization. Works with cross\-functional teams and leverages advanced analytics, applied statistics, AI and ML techniques to drive business insights and optimize operations.
Responsibilities:
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 the 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.
- Designs, builds, and maintains robust data pipelines to collect, clean, and transform data from various sources used in analysis, modeling, and deployed operational environments
- Develops and implements ML models and algorithms to solve complex business problems and improve decision\-making processes across the full life cycle, including problem framing, data collection, data preparation, feature engineering, model selection, training, evaluation, deployment, retraining, and advancement
- Designs and builds AI agents that execute in workflows within enterprise systems (databases, CRMs, ticketing, knowledge bases) and that are deployed with reliable/safety guardrails
- Implements end\-to\-end agent orchestration (prompting, memory/state, tool\-calling, retries/fallbacks) and develops evaluation frameworks (test suites, simulations, human\-in\-the\-loop review) to improve accuracy and reduce error
- Designs, builds, and maintains Retrieval\-Augmented Generation (RAG) GPT applications by integrating enterprise knowledge sources (documents/databases) with embeddings, vector search, and prompt orchestration to deliver accurate, grounded responses with evaluation and safety guardrails
- Analyzes large datasets to uncover trends, patterns, and insights, and creates visualizations and reports to communicate findings to stakeholders
- Monitors and evaluates the performance of data models and systems, and makes necessary adjustments to optimize accuracy and efficiency
- Documents processes, methodologies, and model development to ensure transparency and reproducibility
- Provides training and support to other team members or departments on data tools, techniques, and best practices
- Consults with internal IT teams to ensure infrastructure supports stable, well\-designed, highly available, and well\-maintained Data Science and AI applications
- Stays current with emerging technologies and industry trends to continuously improve data engineering practices and contributes to the development of cutting\-edge solutions
- Ensures the accuracy, consistency, and security of data; implements and enforces data governance policies and best practices.
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 in Computer Science, Analytics, or related field from an accredited college or university. Masters of Science degree preferred. Five (5\) or more years of experience in data engineering, data science, or a related role, with hands\-on experience in building and deploying machine learning models. CERTIFICATES, LICENSES, REGISTRATIONS AND DESIGNATIONS:None KNOWLEDGE, SKILLS AND ABILITIES:* Advanced proficiency in Python and common ML/data libraries such as scikit\-learn, TensorFlow, Keras, PyTorch, Pandas, and NumPy for building, training, and evaluating models
- Strong working knowledge of machine learning methodologies, including supervised learning (e.g., regression, classification) and unsupervised learning (e.g., clustering, dimensionality reduction, anomaly detection)
- Strong SQL skills with experience designing and querying relational databases and supporting data warehousing solutions; familiarity with ETL/ELT workflows and tools (e.g., SSIS or equivalent)
- Working knowledge of medallion architectures
- Skilled in cloud\-based ML development and deployment on platforms such as AWS, Azure, or Google Cloud
- Proficiency with version control and collaborative development workflows, including Git, branching strategies, code review, and basic CI/CD concepts
- Expertise in probability and statistics, including experimental design and hypothesis testing, modeling uncertainty, performance measurement, and selecting appropriate evaluation metrics
- Experience building AI model\-powered applications and workflows using model APIs, including prompt design, tool/function calling, structured outputs (JSON), and response validation/guardrails
- Strong understanding of RAG architectures, including document ingestion pipelines, chunking strategies, metadata design, embedding generation, and retrieval methods
- Hands\-on experience with vector databases/search systems and tuning retrieval for relevance, latency, and cost.
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 periodic performance of moderately physically demanding work, usually involving lifting, carrying, pushing and/or pulling of moderately heavy objects and materials (up to 25 pounds). Tasks that require moving objects of significant weight require the assistance of another person and/or use of proper techniques and
moving equipment. Tasks may involve some climbing, stooping, kneeling, crouching, or crawling. Must be able to safely operate assigned vehicles possibly long distances. 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 and/or outside with 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $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.
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, 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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