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Ml Model Fine Tuning Jobs in Raleigh, NC (NOW HIRING)

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... Hands-on experience developing, fine-tuning, or adapting foundation models for domain-specific data ...

Explore and evaluate new AI/ML techniques, tools, and methodologies, applying relevant innovations ... Hands-on experience developing, fine-tuning, or adapting foundation models for domain-specific data ...

Experience designing production‑grade ML systems with monitoring, evaluation, and observability ... Nd familiarity with large language models and fine tuning Experience with conversational search ...

... ML, feature stores, model monitoring, and drift detection. • Define enterprise patterns for training, fine-tuning, deployment, and observability of ML and GenAI workloads. • Guide teams on ...

Principal AI Architect

Raleigh, NC · On-site

$160K - $240K/yr

... ML, feature stores, model monitoring, and drift detection. * Define enterprise patterns for training, fine-tuning, deployment, and observability of ML and GenAI workloads. * Guide teams on ...

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Ml Model Fine Tuning information

See Raleigh, NC salary details

$9

$67

$139

How much do ml model fine tuning jobs pay per hour?

As of Aug 12, 2026, the average hourly pay for ml model fine tuning in Raleigh, NC is $67.40, according to ZipRecruiter salary data. Most workers in this role earn between $55.14 and $74.76 per hour, depending on experience, location, and employer.

What is the difference between Ml Model Fine Tuning vs Data Scientist?

AspectMl Model Fine TuningData Scientist
CredentialsKnowledge of machine learning frameworks, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentFocus on model optimization, coding, and experimentationData analysis, modeling, and interpretation
Industry UsageAI/ML development teams, tech companiesResearch, analytics, business intelligence

While Ml Model Fine Tuning involves adjusting pre-trained models to improve performance, Data Scientists analyze data, develop models, and interpret results. Fine tuning is a specialized task within the broader scope of a Data Scientist's role, often requiring similar technical skills but focusing more on model optimization.

What are some common challenges faced when fine-tuning machine learning models in a production environment?

One common challenge when fine-tuning ML models in production is ensuring that the updated models generalize well to new, unseen data without overfitting to recent trends or noise. Additionally, coordinating with data engineers and software developers is crucial to maintain data pipelines and model deployment workflows. Managing computational resources and keeping track of model versions for reproducibility can also be complex, especially in fast-paced or large-scale environments. Regular communication with stakeholders is important to align model updates with business objectives and to ensure the smooth integration of improvements.

What is ML model fine-tuning?

ML model fine-tuning is the process of taking a pre-trained machine learning model and making small adjustments to its parameters using new data relevant to your specific task. This approach allows you to leverage the general knowledge the model has already learned, while adapting it to perform better on your particular dataset or problem. Fine-tuning is common in fields like natural language processing and computer vision, as it saves time and resources compared to training a model from scratch. The process typically involves retraining the last few layers of the model or using a lower learning rate for the entire model.

What are the key skills and qualifications needed to thrive as an ML model fine tuning specialist, and why are they important?

To thrive as an ML Model Fine Tuning Specialist, you need a solid background in machine learning, statistics, programming (often Python), and experience with model training and evaluation. Familiarity with frameworks such as TensorFlow, PyTorch, and tools like Hugging Face Transformers, along with experience in managing GPUs and cloud platforms, is typically required. Strong problem-solving skills, attention to detail, and effective communication help you understand project requirements and collaborate with data scientists and engineers. These skills are crucial for optimizing model performance, ensuring accurate results, and delivering robust AI solutions tailored to specific business needs.
What are popular job titles related to Ml Model Fine Tuning jobs in Raleigh, NC? For Ml Model Fine Tuning jobs in Raleigh, NC, the most frequently searched job titles are:
What job categories do people searching Ml Model Fine Tuning jobs in Raleigh, NC look for? The top searched job categories for Ml Model Fine Tuning jobs in Raleigh, NC are:
What cities near Raleigh, NC are hiring for Ml Model Fine Tuning jobs? Cities near Raleigh, NC with the most Ml Model Fine Tuning job openings:
Infographic showing various Ml Model Fine Tuning job openings in Raleigh, NC as of July 2026, with employment types broken down into 58% Full Time, and 42% Contract. Highlights an 49% In-person, and 51% Remote job distribution, with an average salary of $140,186 per year, or $67.4 per hour.

Senior AWS/Cloud Full Stack Engineer

INFOSYS NOVA HOLDINGS LLC

Raleigh, NC • On-site

Full-time

Re-posted 21 days ago


Job description

Description:

Senior AWS/Cloud Full Stack Engineer – Role Requirements

Technical Expertise

Frontend Development: Proficiency in modern frameworks such as React, Angular; strong understanding of HTML, CSS, JavaScript/TypeScript.

Backend Development: Experience with server-side technologies like Node.js, Python, Java (SpringBoot)

Database Management: Strong skills in relational databases (e.g., PostgreSQL, MySQL) andfamiliarity with NoSQL databases (e.g., DocumentDB/MongoDB).

API Design & Integration: Expertise in building and consuming RESTful and GraphQL APIs.

Cloud & DevOps: Experience with AWS cloud platform, CI/CD pipelines, containerization (Docker,Kubernetes), and infrastructure-as-code tools.

GenAI & Emerging Tech

Exposure to Generative AI technologies, including experience integrating LLMs (e.g., OpenAI,Anthropic) into applications.

Familiarity with prompt engineering, model fine-tuning, or building GenAI-powered features such aschatbots, summarization tools, or intelligent assistants.

Understanding of ethical considerations and responsible AI practices in GenAI implementations.

Software Engineering Practices

Strong understanding of software design principles, architecture patterns, and system scalability.

Experience with automated testing frameworks and test-driven development (TDD).

Familiarity with version control systems (e.g., Git) and agile development methodologies.

Leadership & Collaboration

Proven ability to lead technical initiatives and mentor junior developers.

Strong communication skills to collaborate effectively with cross-functional teams including Product,UX, and QA.

Ability to translate business requirements into scalable technical