AI ML Engineer Jobs
AI ML Engineer jobs are open across technology, healthcare, financial services, and defense, from entry-level to staff and principal, with specializations in deep learning, natural language processing, and computer vision. Find a role that fits from the openings below and apply directly.
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Showing 5 of 331+ AI ML Engineer jobs
INTRODUCTION
Provide hands-on technical leadership by designing, developing, and deploying ML/LLM/GenAI solutions from concept through production, maintaining ownership for reliability and operability once deployed. Work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases. Mentor and uplift junior engineers through design reviews, code reviews, pairing, and coaching, raising engineering quality and delivery discipline across the team. You will build and institutionalize MLOps capabilities, including automated pipelines for deployment, monitoring, and model lifecycle management, with emphasis on scalability and reliability. Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation. Conduct thorough evaluations of generative models (e.g., GPT-4.1), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. Implement monitoring mechanisms to track model performance in real-time and ensure model reliability. Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences. Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
BASIC QUALIFICATIONS
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- 10+ years of engineering experience, including 3-5+ years building, deploying, and operating applied AI/ML systems in production (model lifecycle, MLOps, monitoring, and governance)
- Demonstrate hands-on engineering leadership: setting technical direction, making architecture decisions, conducting design and code reviews, mentoring junior engineers, and guiding implementation quality across multiple workstreams
- Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API
- Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API
- Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization
- Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs
- Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications
- Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects
- A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering
PREFERRED QUALIFICATIONS
- Familiarity with the financial services industries
- Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG)
- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies
INTRODUCTION
Provide hands-on technical leadership by designing, developing, and deploying ML/LLM/GenAI solutions from concept through production, maintaining ownership for reliability and operability once deployed. Work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases. Mentor and uplift junior engineers through design reviews, code reviews, pairing, and coaching, raising engineering quality and delivery discipline across the team. You will build and institutionalize MLOps capabilities, including automated pipelines for deployment, monitoring, and model lifecycle management, with emphasis on scalability and reliability. Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation. Conduct thorough evaluations of generative models (e.g., GPT-4.1), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. Implement monitoring mechanisms to track model performance in real-time and ensure model reliability. Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences. Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
BASIC QUALIFICATIONS
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- 10+ years of engineering experience, including 3-5+ years building, deploying, and operating applied AI/ML systems in production (model lifecycle, MLOps, monitoring, and governance)
- Demonstrate hands-on engineering leadership: setting technical direction, making architecture decisions, conducting design and code reviews, mentoring junior engineers, and guiding implementation quality across multiple workstreams
- Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API
- Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API
- Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization
- Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs
- Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications
- Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects
- A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering
PREFERRED QUALIFICATIONS
- Familiarity with the financial services industries
- Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG)
- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies
AI ML Engineer Jobs by Experience Level
Top Cities Hiring AI ML Engineers
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Find AI ML Engineer JobsAI ML Engineer Job Market
Who's Hiring
- Apple25
- JPMorganChase25
- Booz Allen Hamilton11
- Optum10
- General Motors (GM)10
Top Industries Hiring
- Technology & Software34
- Electronics & Hardware20
- Banking & Financial Services20
- Insurance13
- Consulting & Professional Services12
What Employers Look For
The qualifications that appear most often in AI ML engineer jobs.
- Proficiency in Python and at least one ML framework such as PyTorch or TensorFlow
- Experience designing, training, and deploying machine learning models in production environments
- Strong foundation in statistics, linear algebra, and probability theory
- Familiarity with MLOps tools and platforms including Kubeflow, MLflow, or SageMaker
- Experience working with large datasets using SQL, Spark, or distributed computing frameworks
- Bachelor's or master's degree in computer science, mathematics, or a related quantitative field
Tips for Your AI ML Engineer Job Search
Tailor your resume to each stack
Recruiters scan for specific frameworks like PyTorch, TensorFlow, or JAX before reading anything else. Match the exact tool names listed in the job description, and call out model types you've trained or fine-tuned rather than listing ML as a general skill.
Show inference costs, not just accuracy
Most job descriptions ask for production ML experience. Quantify latency improvements, model compression ratios, or infrastructure cost reductions you've driven. Accuracy metrics alone don't signal you've shipped a model that runs reliably at scale.
Target openings by model domain, not job title
Titles vary wildly across companies. Search for the domain you work in, like recommendation systems, time-series forecasting, or LLM fine-tuning, in addition to the job title. You'll surface relevant roles that use different naming conventions.
Apply early to roles that fit
Migrate Mate lists ai ml engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare a system design answer for ML pipelines
Most ai ml engineer interviews include a round where you design a full ML system end to end. Practice walking through data ingestion, feature engineering, training infrastructure, model serving, and monitoring before you get on the call.
Negotiate on compute budget, not just compensation
When you reach the offer stage, ask about GPU or TPU access, cloud credits, and whether the team uses managed services or builds infrastructure internally. These details affect your day-to-day work and your ability to ship, and they're fully negotiable.
AI ML Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most ai ml engineers?
The companies hiring the most ai ml engineers right now include Apple, JPMorganChase, and Booz Allen Hamilton, with the largest share of openings in California, Virginia, and New York, based on current listings on Migrate Mate as of August 2026. Demand is concentrated in companies building large-scale recommendation systems, generative AI products, and enterprise ML platforms.
How many ai ml engineer jobs are remote?
About 57% of ai ml engineer openings are fully remote or hybrid as of August 2026, making it one of the more flexible engineering roles available. Research and applied science positions tend to offer the most remote flexibility, while roles tied to real-time inference infrastructure or on-premise hardware more often require on-site presence.
How do you become an ai ml engineer?
Build a foundation in Python, linear algebra, and statistics, then work through core ML concepts using publicly available courses and datasets. Develop hands-on projects that go beyond training a model, covering feature pipelines, evaluation, and deployment. Contribute to open-source ML projects or Kaggle competitions to build a visible portfolio, then apply to roles that match your domain focus.
Can you get an ai ml engineer job with little or no experience?
Yes, but you need a portfolio that demonstrates you can move a model from experiment to production. Build end-to-end projects that include data preprocessing, model selection, evaluation, and a deployed endpoint. Entry-level roles and ML engineering apprenticeships at product companies are the most accessible starting points for candidates without formal industry experience.
What does the ai ml engineer interview process look like?
Most ai ml engineer interview processes include a recruiter screen, a take-home or live coding round focused on Python and data manipulation, an ML system design round where you architect a full pipeline, and a behavioral round. Senior roles often add a research presentation or a deep-dive into a past project you've shipped, with questions on trade-offs and production challenges.
Where can I find and apply to ai ml engineer jobs?
You can find and apply to ai ml engineer jobs on Migrate Mate, which lists current openings from companies across the United States. Search for roles that match your specialization and experience level, then apply directly to each listing that fits.
See All 331+ AI ML Engineer Jobs
Find roles that match your experience and apply in just a few clicks.
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