Generative AI Engineer Jobs
Generative AI Engineer jobs are open across tech, financial services, healthcare, and media, from new-grad to staff and principal levels, with specializations in large language model fine-tuning, retrieval-augmented generation, and multimodal AI systems. Find a role that fits from the openings below and apply directly.
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Showing 5 of 53+ Generative AI Engineer jobs
Title: Senior Developer
Location: Columbia, SC - Partially onsite - 3 days per week (Tue, Wed, Thurs) and as needed.
Duration: 11 months
Standard work hours: 8:00 AM – 5:00 PM
Credit check will be required
Job Description:
This is a new role to establish a core competency in agentic AI systems. This engineer will be pivotal in designing and deploying advanced AI agents and will build the foundational frameworks for future AI use cases across the organization.
Day to Day:
- A typical day will involve a mix of hands-on coding, architectural design, and research. The engineer will spend a significant portion of their time in Python, building and optimizing agentic AI systems using frameworks like LangChain.
- This includes integrating these agents with our backend services and deploying them using CI/CD pipelines into our cloud environment.
- They will also be responsible for researching and testing new agentic models and frameworks, monitoring agent behavior in production, and collaborating with data scientists and business stakeholders to refine requirements and ensure the ethical deployment of AI solutions.
Team:
The team is an innovative, collaborative, and empowering environment. We are building the next generation of AI solutions for the enterprise in a fast-paced, project-oriented setting. This is a multi-platformed environment that values creativity, continuous learning, and a customer-focused mindset. The new engineer will play a crucial role in shaping our AI strategy and building foundational tools and accelerators that will drive innovation across the company.
NOT Looking for:
We are not looking for candidates whose primary experience is in data analytics or traditional machine learning (e.g., regression, classification) without significant exposure to Generative AI.
Minimum Required Education:
Bachelor's degree in Computer Science, Information Technology or other job related degree or 4 years relevant experience or Associates degree + 2 years relevant experience
Minimum Required Work Experience:
8 years of application development, systems testing or other job related experience. (6 yrs for Dev IV)
Required Technologies:
- 3–6 years of hands-on experience in Artificial Intelligence, Machine Learning, or related fields.
- Python & AI/ML Libraries: Deep hands-on experience in Python for AI/ML development.
- Generative AI Development: Proven experience developing Gen AI or AI/ML solutions, from use case conceptualization to production deployment.
- Infrastructure & DevOps: Strong understanding of cloud environments (AWS preferred), LLM hosting, CI/CD pipelines, Docker, and Kubernetes.
- Agentic AI Concepts: Knowledge of agentic/autonomous systems (e.g., reasoning, planning, tool use).
Tech Stack:
- Python
- JavaScript/TypeScript
- AI Tools and Libraries (e.g. LangGraph, LangChain, Deep Agents, Claude Skills, etc.)
- AI Models (e.g. Claude, OpenAI, etc.)
- AI Concepts (e.g. Prompt Engineering, RAG, Agentic AI, etc.)
- Distributed SDLC/DevOps (e.g. github, pipelines, VS Code, testing frameworks, etc.)
- Platforms (Container Platforms, Cloud Platforms, Document Databases, AWS)
- API Design
Nice to Have:
- Proficiency in Python development and FastAPI/Flask frameworks, along with SQL.
- Familiarity with agentic AI frameworks and concepts such as LangChain, LangGraph, AutoGen, Model Context Protocol (MCP), Chain of Thought prompting, knowledge stores, and embeddings.
- Experience developing autonomous agents using cloud-based AI services.
- Experience with prompt engineering techniques and model fine-tuning.
- Strong understanding of reinforcement learning, planning algorithms, and multi-agent systems.
- Experience working across cloud platforms (AWS, Azure, GCP) and deploying AI solutions at scale.
Title: Senior Developer
Location: Columbia, SC - Partially onsite - 3 days per week (Tue, Wed, Thurs) and as needed.
Duration: 11 months
Standard work hours: 8:00 AM – 5:00 PM
Credit check will be required
Job Description:
This is a new role to establish a core competency in agentic AI systems. This engineer will be pivotal in designing and deploying advanced AI agents and will build the foundational frameworks for future AI use cases across the organization.
Day to Day:
- A typical day will involve a mix of hands-on coding, architectural design, and research. The engineer will spend a significant portion of their time in Python, building and optimizing agentic AI systems using frameworks like LangChain.
- This includes integrating these agents with our backend services and deploying them using CI/CD pipelines into our cloud environment.
- They will also be responsible for researching and testing new agentic models and frameworks, monitoring agent behavior in production, and collaborating with data scientists and business stakeholders to refine requirements and ensure the ethical deployment of AI solutions.
Team:
The team is an innovative, collaborative, and empowering environment. We are building the next generation of AI solutions for the enterprise in a fast-paced, project-oriented setting. This is a multi-platformed environment that values creativity, continuous learning, and a customer-focused mindset. The new engineer will play a crucial role in shaping our AI strategy and building foundational tools and accelerators that will drive innovation across the company.
NOT Looking for:
We are not looking for candidates whose primary experience is in data analytics or traditional machine learning (e.g., regression, classification) without significant exposure to Generative AI.
Minimum Required Education:
Bachelor's degree in Computer Science, Information Technology or other job related degree or 4 years relevant experience or Associates degree + 2 years relevant experience
Minimum Required Work Experience:
8 years of application development, systems testing or other job related experience. (6 yrs for Dev IV)
Required Technologies:
- 3–6 years of hands-on experience in Artificial Intelligence, Machine Learning, or related fields.
- Python & AI/ML Libraries: Deep hands-on experience in Python for AI/ML development.
- Generative AI Development: Proven experience developing Gen AI or AI/ML solutions, from use case conceptualization to production deployment.
- Infrastructure & DevOps: Strong understanding of cloud environments (AWS preferred), LLM hosting, CI/CD pipelines, Docker, and Kubernetes.
- Agentic AI Concepts: Knowledge of agentic/autonomous systems (e.g., reasoning, planning, tool use).
Tech Stack:
- Python
- JavaScript/TypeScript
- AI Tools and Libraries (e.g. LangGraph, LangChain, Deep Agents, Claude Skills, etc.)
- AI Models (e.g. Claude, OpenAI, etc.)
- AI Concepts (e.g. Prompt Engineering, RAG, Agentic AI, etc.)
- Distributed SDLC/DevOps (e.g. github, pipelines, VS Code, testing frameworks, etc.)
- Platforms (Container Platforms, Cloud Platforms, Document Databases, AWS)
- API Design
Nice to Have:
- Proficiency in Python development and FastAPI/Flask frameworks, along with SQL.
- Familiarity with agentic AI frameworks and concepts such as LangChain, LangGraph, AutoGen, Model Context Protocol (MCP), Chain of Thought prompting, knowledge stores, and embeddings.
- Experience developing autonomous agents using cloud-based AI services.
- Experience with prompt engineering techniques and model fine-tuning.
- Strong understanding of reinforcement learning, planning algorithms, and multi-agent systems.
- Experience working across cloud platforms (AWS, Azure, GCP) and deploying AI solutions at scale.
See All 53+ Generative AI Engineer Jobs
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Find Generative AI Engineer JobsGenerative AI Engineer Job Market
A snapshot from current openings nationwide, updated as new roles post.
Who's Hiring
- NVIDIA6
- Cognizant4
- Adobe3
- Tiger Analytics3
- Citi2
Top Industries Hiring
- Technology & Software28
- Consulting & Professional Services14
- Artificial Intelligence4
- Banking & Financial Services3
- Electronics & Hardware3
What Employers Look For
The qualifications that appear most often in generative AI engineer jobs.
- Proficiency in Python and deep learning frameworks such as PyTorch or JAX
- Hands-on experience fine-tuning or prompting large language models like GPT, Llama, or Gemini
- Experience building and deploying retrieval-augmented generation pipelines
- Familiarity with model serving infrastructure including APIs, containerization, and cloud platforms
- Bachelor's or master's degree in computer science, machine learning, or a related field
- Understanding of evaluation frameworks, safety considerations, and responsible AI practices
Tips for Your Generative AI Engineer Job Search
Tailor your resume to model type
Recruiters and hiring managers scan for specific model families you've worked with. Call out LLMs, diffusion models, or multimodal architectures by name in your experience bullets, not just 'generative AI,' so your resume matches the exact language in job listings.
Show production deployments, not prototypes
Most generative AI engineer roles want evidence of systems that shipped, not Jupyter notebooks. Quantify throughput, latency improvements, or cost reductions from a deployed endpoint. Side projects count if they serve real users and you can describe the infrastructure.
Filter openings by stack before applying
Job descriptions vary widely: some teams run everything on proprietary APIs, others want low-level PyTorch experience. Read the technical requirements carefully and match your application to roles where your stack overlaps at least two thirds of what they list.
Apply early to roles that fit
Migrate Mate lists generative ai engineer openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare for system design around inference
Technical interviews at companies building generative AI products often focus on inference pipeline design: batching strategies, model serving frameworks like vLLM or TGI, and cost-per-token tradeoffs. Practicing these scenarios is more valuable than re-reading model architecture papers.
Negotiate scope before salary
Generative AI roles vary enormously in autonomy. Before discussing compensation, clarify whether you own the full ML lifecycle or support a research team. Roles with more ownership over architecture and deployment decisions typically have stronger leverage for negotiating total compensation.
Generative AI Engineer Jobs: Frequently Asked Questions
Which companies are hiring the most generative ai engineers?
The companies hiring the most generative ai engineers right now include NVIDIA, Cognizant, and Adobe, with the largest share of openings in California, Texas, and New York, based on current listings on Migrate Mate as of June 2026. Demand is concentrated at companies building AI-native products as well as enterprises integrating generative AI into existing platforms.
How many generative ai engineer jobs are remote?
About 34% of generative ai engineer openings are fully remote or hybrid as of June 2026, making it one of the more flexible engineering disciplines. Roles focused on prompt engineering, API integration, and evaluation tend to be most commonly offered remotely, while positions involving proprietary infrastructure or on-premise model deployment are more likely to require on-site presence.
How do you become a generative ai engineer?
Start by building a strong foundation in Python and machine learning fundamentals, then move into hands-on work with transformer architectures and pre-trained models through open-source projects or coursework. Practice fine-tuning models on domain-specific datasets, build at least one end-to-end application that uses a generative model in production, and document the infrastructure decisions you made along the way. Familiarity with vector databases, prompt engineering patterns, and model evaluation methods rounds out the core skill set employers look for.
Can you get a generative ai engineer job with little experience?
Yes, entry-level generative AI roles exist, and employers hiring at that level prioritize demonstrated projects over years of experience. Build a public portfolio that shows you've integrated an LLM into a real application, contributed to an open-source AI project, or fine-tuned a model on a specific dataset. Roles titled AI engineer, ML engineer, or applied AI developer often have lower experience bars and serve as strong entry points into the field.
What does the generative ai engineer interview process look like?
Most generative AI engineer interviews include a recruiter screen followed by a technical phone interview covering Python, ML concepts, and prior project experience. A take-home or live coding assessment typically tests your ability to build or evaluate a generative AI component, such as a retrieval pipeline or an evaluated prompt chain. Final rounds usually include a system design interview focused on inference infrastructure and a cross-functional conversation with product or research stakeholders about your approach to model tradeoffs and safety.
Where can I find and apply to generative ai engineer jobs?
You can find and apply to generative ai engineer jobs on Migrate Mate, which lists current openings from across the United States. Search the listings to find roles that match your experience and specialization, then apply directly to each one that fits. The platform pulls in openings from a wide range of companies, so you can compare roles across industries without searching multiple sites.
See All 53+ Generative AI Engineer Jobs
Jump back to the full list of openings and apply to any generative AI engineer role that fits.
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