AI ML Engineering Jobs
AI ML Engineering jobs are open across technology, healthcare, finance, and manufacturing, at every level from new-grad to principal and staff, 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 187+ AI ML Engineering 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 Engineering Jobs by Experience Level
Top Cities Hiring AI ML Engineerings
Explore AI ML engineering openings in the cities hiring most right now.
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Find AI ML Engineering JobsAI ML Engineering Job Market
Who's Hiring
- JPMorganChase23
- Optum10
- Booz Allen Hamilton9
- General Motors (GM)9
- Amazon Web Services8
Top Industries Hiring
- Technology & Software22
- Banking & Financial Services14
- Electronics & Hardware10
- Consulting & Professional Services9
- Investment & Asset Management7
What Employers Look For
The qualifications that appear most often in AI ML engineering jobs.
- Proficiency in Python and at least one major ML framework such as PyTorch or TensorFlow
- Experience building, training, and deploying machine learning models in production environments
- Strong understanding of statistics, probability, and core machine learning algorithms
- Familiarity with cloud platforms such as AWS, Google Cloud, or Azure for model serving
- Experience with data pipelines, feature engineering, and large-scale data processing tools
- Bachelor's or master's degree in computer science, mathematics, statistics, or a related field
Tips for Your AI ML Engineering Job Search
Tailor your resume to each job
AI ML engineering listings vary sharply in stack and method. Match your resume's technical section to the exact frameworks named in the posting, whether that's PyTorch, TensorFlow, JAX, or scikit-learn, so automated screening tools rank you higher before a human ever reads it.
Apply early to roles that fit
Migrate Mate lists ai ml engineering openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Lead with measurable model outcomes
Hiring managers in ai ml engineering care about impact, not methodology alone. Quantify results where you can: latency improvements, accuracy gains, or cost reductions your models produced. A percentage improvement on a production system outweighs a list of libraries you've used.
Build a public project portfolio
Many ai ml engineering roles screen candidates by asking for a GitHub or similar link before a phone screen. Post at least two end-to-end projects, including data preprocessing, training code, evaluation, and a short write-up explaining the tradeoffs you made.
Prepare for the full interview loop
AI ML engineering interviews typically combine coding rounds, ML system design, and a take-home or live modeling exercise. Practice designing scalable serving pipelines and explaining regularization or feature engineering decisions out loud, not just writing code on a whiteboard.
Negotiate with competing offers strategically
AI ML engineering is one of the few fields where counter-offers are expected. If you have multiple final-round processes running simultaneously, let each recruiter know you're actively interviewing elsewhere. Concrete timelines, not vague interest, motivate faster decisions and better offers.
AI ML Engineering Jobs: Frequently Asked Questions
Which companies are hiring the most ai ml engineerings?
The companies hiring the most ai ml engineerings right now include JPMorganChase, Optum, and Booz Allen Hamilton, with the largest share of openings in California, Virginia, and Texas, based on current listings on Migrate Mate as of August 2026. Demand is concentrated in technology, healthcare, and financial services sectors.
How many ai ml engineering jobs are remote?
About 53% of ai ml engineering openings are fully remote or hybrid as of August 2026, making it one of the more remote-friendly engineering disciplines. Research-focused and NLP roles tend to have the highest share of remote flexibility, while roles requiring access to proprietary on-premise data infrastructure are more likely to be on-site.
How do you become a ai ml engineering?
Start by building a strong foundation in Python, linear algebra, calculus, and probability. Work through core machine learning concepts using structured courses or university programs, then implement projects end to end, from raw data to a deployed model. Contribute to open-source repositories or Kaggle competitions to demonstrate applied skills. Pursue a degree or practical bootcamp in computer science, data science, or statistics if you haven't already, and keep current with research papers in your area of focus.
Can I get an ai ml engineering job with little or no experience?
Entry-level ai ml engineering roles do exist, and the path in typically runs through a strong public portfolio rather than years of industry experience. Build two or three end-to-end projects covering different problem types such as classification, regression, or generative modeling, and document your design choices clearly. Internships, research assistant positions, and ML-adjacent roles in data analytics or software engineering are common stepping stones that give you production exposure before moving into a dedicated ML role.
What does the ai ml engineering interview process look like?
Most ai ml engineering interview loops include a recruiter screen, one or two coding rounds focused on data structures and algorithms, an ML fundamentals round covering topics like bias-variance tradeoff and model evaluation, and an ML system design round where you architect a scalable training or serving pipeline. Many companies also include a take-home modeling exercise or a live case study. Final rounds often involve a presentation to the team or a culture-fit conversation with senior engineers or managers.
Where can I find and apply to ai ml engineering jobs?
You can find and apply to ai ml engineering jobs on Migrate Mate, which lists current openings from across the United States in one place. Search the available roles, find the ones that match your skills and seniority level, and apply directly to each listing from the page.
See All 187+ AI ML Engineering Jobs
Find roles that match your experience and apply in just a few clicks.
Find AI ML Engineering Jobs