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About This Role
*About the Role*
We are seeking a highly skilled Senior Machine Learning Engineer to join our growing AI team. In this role, you will design, develop, deploy, and maintain Machine Learning and Artificial Intelligence solutions that drive business value through intelligent automation, predictive analytics, and innovative AI\-powered products.
You will work closely with Data Scientists, Data Engineers, Software Engineers, and business stakeholders to transform experimental models into scalable, production\-ready solutions.
*Key Responsibilities*
- Design, train, optimize, and deploy Machine Learning models in production environments.
- Develop Computer Vision solutions for image and video detection, classification, and segmentation.
- Build and maintain scalable data pipelines for model training and inference.
- Integrate Machine Learning models through APIs and microservices.
- Select, evaluate, and adapt Large Language Models (LLMs) for business use cases.
- Implement Prompt Engineering, Retrieval\-Augmented Generation (RAG), and fine\-tuning techniques.
- Develop Generative AI solutions, including intelligent assistants, automated summarization, and recommendation systems.
- Monitor model performance, drift, and retraining processes.
- Optimize cloud infrastructure costs and model performance across AWS, Azure, or GCP environments.
- Collaborate within Agile/Scrum teams to deliver high\-quality AI solutions.
- Ensure best practices in software development, scalability, maintainability, and documentation.
- Partner with cross\-functional teams to translate business requirements into AI\-driven solutions.
*Required Qualifications*
- Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Software Engineering, or a related field.
- Master’s degree or postgraduate specialization in Machine Learning, Artificial Intelligence, or related disciplines is highly preferred.
*Professional Experience*
- 5\+ years of proven experience as a Machine Learning Engineer.
- Hands\-on experience developing and deploying Machine Learning solutions in production environments.
- Strong background in Computer Vision projects.
- Experience building scalable AI systems in cloud environments.
- Experience working in Agile/Scrum environments.
*Technical Skills*
Machine Learning \& Data Science
- Advanced proficiency in Python.
- Strong experience with NumPy, Pandas, and related data processing libraries.
- Expertise in supervised and unsupervised learning techniques.
- Experience with TensorFlow, PyTorch, and Scikit\-learn.
- Strong understanding of model training, evaluation, optimization, and deployment.
Computer Vision
- Experience with OpenCV, Pillow, Albumentations, or similar frameworks.
- Knowledge of image classification, object detection, segmentation, and video analytics.
Software Engineering
- Experience developing APIs using FastAPI and/or Flask.
- Proficiency with Git and version control best practices.
- Strong knowledge of SQL and NoSQL databases.
- Experience building scalable microservices architectures.
Cloud \& MLOps
- Experience deploying Machine Learning solutions on AWS, Azure, or Google Cloud Platform.
- Familiarity with MLflow, Kubeflow, Airflow, Vertex AI Pipelines, and/or AWS SageMaker.
- Understanding of CI/CD practices for Machine Learning systems.
Generative AI \& LLMs
- Hands\-on experience with LLMs such as GPT, Llama, Mistral, Claude, Falcon, or Gemini.
- Experience with Prompt Engineering and Retrieval\-Augmented Generation (RAG).
- Knowledge of fine\-tuning techniques and model optimization strategies.
- Familiarity with LoRA, quantization, distillation, and other cost\-optimization approaches.
- Experience building AI\-powered assistants, chatbots, and multimodal applications.
*Preferred Certifications*
- AWS Certified Machine Learning – Specialty
- Microsoft Certified: Azure Data Scientist Associate
- Google Cloud Professional Machine Learning Engineer
Pay: $14,200\.00 \- $15,800\.00 per month
Work Location: Hybrid remote in Austin, TX 78789
Salary Context
This $170K-$189K range is below the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 roles with salary data).
View full AI/ML Engineer salary data →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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Key Prediction, 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 $178,940 based on 11,900 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $170K to $189K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Key Prediction AI Hiring
Key Prediction has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US. Compensation range: $189K - $189K.
Location Context
AI roles in Austin pay a median of $218,800 across 493 tracked positions. That's 9% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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