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Maruti

Maruti

LLM Developer

Experience12 years
AvailabilityFull-time

Maruti has 12+ years of experience specializing in developing products with Machine Learning, LLM, Computer Vision, Time Series Modeling, Predictive Modeling, and Deep Learning. He has managed multiple cross-team projects.

Expert in
  • LLM
  • Image Recognition
  • Machine Learning
  • Computer Vision
  • Data Analysis
Also worked with
  • AWS
  • Cloud
  • Numpy
  • Deep Learning
Javier

Javier

LLM Developer

Experience4 years
AvailabilityFull-time

Javier is a machine learning professional certified by IMB. He has extensive experience in natural language processing, large language models, regression models and recommendations systems.

Expert in
  • LLM
  • Latex
  • Machine Learning
  • Data Analysis
Also worked with
  • Python
  • Deep Learning
George Shakan

George

GPT Developer

Experience3 years
AvailabilityFull-time

George is an experienced data scientist with a Phd. in Mathematics and he has strong problem-solving skills and experience building machine learning and LLM solutions. He has previous experience in working with GPT-based solutions and has a strong grasp of Python libraries.

Expert in
  • GPT
  • Machine learning
  • Data Science
  • Python
Also worked with
  • AWS
  • SQL
  • NumPy
  • PyTorch
Santiago

Santiago

LLM Developer

Experience3 years
AvailabilityFull-time

Santiago is a data scientist with 3+ years of experience working with statistical models, simulations, and artificial intelligence. He is proficient in building applications powered by LLMs (Lanchain,OpenAi).

Expert in
  • LLM
  • Data Analysis
  • Matplotlib
  • Numpy
  • Python
Also worked with
  • Arduino
  • SQL
  • Data Engineering
Arsenii

Arsenii

LLM Developer

Experience5 years
AvailabilityFull-time

Arsenii is a data scientist with 5 years of experience in creating advanced solutions for computer vision, data analysis, and natural language processing.

Expert in
  • LLM
  • Python
  • PyTorch
  • Git
  • Machine Learning
Also worked with
  • Bash
  • Computer Vision
  • SQL
  • Tensorflow
Justin Menga

Justin

GPT Developer

Experience20 years
AvailabilityFull-time

Justin is an experienced engineer, developer, architect, and tech lead with 20+ years of experience in architecture, design, and building new products. He is proficient in GPT, NLP, machine learning, data science, Python, Cloud, and React among other skills.

Expert in
  • GPT
  • NLP
  • Machine learning
  • Python
Also worked with
  • Java
  • React
  • DevOps
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Guide to hiring remote LLM developers

Hire LLM developers that go above and beyond to deliver excellence. Leverage our well-curated guide on the skills to look for, interview questions, and more.

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Guide to hiring remote LLM developers
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Hire LLM developers through Turing in 4 easy steps

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Hiring LLM developers: Everything you need to know

If you’re looking for a highly experienced LLM engineer, this section explores the role and responsibilities of an LLM developer.Large language models (LLMs) have emerged as a transformative force in optimizing business operations and boosting productivity. LLMs are cutting-edge artificial intelligence models trained on vast amounts of text data, enabling them to understand and generate human-like language. These models can generate text and comprehend complex programming languages, algorithms, and software development concepts.

Who is an LLM developer?

An LLM developer is a software developer who specializes in working with large language models (LLMs). These developers deeply understand the underlying principles and techniques used in training and utilizing LLMs. They are proficient in programming languages and frameworks commonly used in natural language processing (NLP) tasks. LLM developers leverage the power of LLMs to fine-tune pre-trained models, adapt them to specific domains or tasks, and develop custom applications.

Why should you hire LLM developers?

LLM developers are valuable assets for companies seeking to harness the capabilities of LLMs to enhance their software applications, automate tasks, optimize processes, and turbocharge productivity. Here are prominent reasons why you should hire LLM developers:

  • Improved customer experience: LLM developers can help businesses improve their customer experience by developing AI-powered chatbots and virtual assistants. These tools can provide instant, accurate responses to customer queries, improving customer satisfaction and loyalty .
  • Efficient automation: LLM developers can develop custom applications powered by LLMs to automate time-consuming tasks such as generating reports, writing marketing copies, and translating languages, helping employees focus on strategic initiatives.
  • LLM-powered coding assistant tools: LLM developers can create or implement AI-powered coding assistants to help other developers write code more efficiently. These tools can provide real-time suggestions, detect errors, and even automate parts of the coding process. This can significantly speed up software development, reduce the likelihood of errors, and free up developers to focus on more complex tasks.
  • New product and service development: LLMs can be used to generate new ideas for products and services and prototype and test them. This can help businesses stay ahead of the competition and meet the changing needs of their customers.
  • Improved decision-making: LLMs can be used to analyze large amounts of data and identify patterns and trends that would be difficult or impossible for humans to detect. This can help businesses to make better decisions on product features and marketing campaigns.

Benefits of hiring Turing LLM developers

Turing offers deeply vetted, highly experienced LLM engineers with expertise in machine learning, NLP algorithms, programming languages such as Python, Java, C++, libraries such as Tensorflow and Pytorch, and cloud computing platforms such as AWS, GCP, and Azure. We also vet LLM developers for communication and seniority skills to ensure they integrate seamlessly with your team.

Turing has deployed highly qualified LLM trainers at scale on numerous projects involving training data generation for LLM coding tasks over the last 2 years. Our developers offer LLM fine tuning and reinforcement learning from human feedback (RLHF) capabilities with a high degree of efficiency and agility. 

We also provide a fully managed team of LLM developers, trainers, and data scientists  allowing researchers to focus solely on task design while Turing's management handles developer coordination and management. With our proficiency in ML, NLP, and LLMs, we have established ourselves as a leading vendor in delivering comprehensive LLM solutions and developers to meet our clients' varying LLM needs.

When you hire LLM engineers from Turing, you get the following benefits:

  • Access to global developer pool
  • Rigorously vetted LLM developers and trainers
  • Hire LLM developers within 4 days
  • 14-day risk-free trial period
  • Productivity monitoring
  • Global payments
  • Developer support
  • Secure development environment
  • SOC2 compliance

Sign up today and get access to the best remote LLM engineers.

Roles and responsibilities of LLM developers

If you’re looking for a highly experienced LLM engineer, this section explores the role and responsibilities of an LLM developer.

  • Model development: LLM developers are responsible for designing, fine-tuning, and implementing large language models. This involves selecting appropriate architectures, training models on large datasets, fine-tuning models, and optimizing their performance.
  • Data pre-processing: LLM developers handle data preprocessing tasks, which include cleaning and preparing large amounts of text data for training the models. This may involve tasks such as tokenization, normalization, and data augmentation.
  • Algorithm design and implementation: LLM developers design and implement algorithms and techniques for natural language processing (NLP) tasks, such as text classification, sentiment analysis, named entity recognition, and machine translation. They leverage their NLP and machine learning expertise to develop efficient and accurate models.
  • Model evaluation and improvement: LLM developers evaluate the performance of their models using appropriate metrics and techniques. They analyze the strengths and weaknesses of the models and iterate on them to improve their accuracy, fluency, and coherence.
  • Deployment and integration: LLM engineers deploy large language models into production environments, often on cloud platforms. They also integrate these models into existing software systems, applications, or websites to help companies leverage the power of AI in their specific use cases.
  • Collaboration and communication: LLM developers collaborate with cross-functional teams, including researchers, engineers, product managers, and stakeholders. They communicate effectively to understand requirements, discuss technical challenges, and provide updates on the progress of their work.

Skills to look for when hiring LLM developers

When hiring LLM developers, you must look for proficiency in the following skills:

1. Expertise in natural language processing (NLP)

Look for candidates with a solid understanding of NLP techniques, algorithms, and models. They should be familiar with text classification, sentiment analysis, named entity recognition, and machine translation tasks.

2. Proficiency in machine learning

Candidates should have a strong foundation in ML concepts and techniques. Look for experience with deep learning architectures like recurrent neural networks (RNNs), transformers, and attention mechanisms. Knowledge of frameworks such as TensorFlow or PyTorch is highly recommended.

3. Experience in relevant programming languages

LLM developers should be proficient in programming languages commonly used in AI development, such as Python, Java, or C++. They should be experienced in writing well-structured, efficient, consistent, and maintainable code. Familiarity with relevant libraries and frameworks is a plus.

4. Understanding of data handling and pre-processing

Look for candidates with experience working with large datasets and skilled in data preprocessing tasks. They should be adept at cleaning, transforming, and augmenting text data to prepare it for training LLMs.

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5. Familiarity with model development and finetuning

Candidates should have hands-on experience in developing and fine-tuning LLMs. They should be able to select appropriate architectures, train models on large datasets, and optimize their performance using techniques like retrieval augmented generation (RAG, and transfer learning, among others.

6. Knowledge of evaluation metrics

Look for candidates who understand how to evaluate the performance of LLMs using appropriate metrics such as BERTScore, MoverScore, perplexity, and BLEUScore, among others. They should be able to analyze and interpret evaluation results to improve the performance of the models.

7. Communication and collaboration skills

Effective collaboration and communication skills are crucial for LLM developers. They should be able to work well in cross-functional teams, communicate technical concepts clearly, and actively participate in discussions and knowledge sharing.

Work with top LLM developers from around the world

Turing helps you find the right developers for your project

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Create a hiring funnel

Creating a hiring funnel will provide you with numerous benefits, like assisting you in identifying the top skills and identifying the LLM developers who fit into your company's culture.

What Turing does for you

Candidates screening
Candidates screening

We will help you select the best talents and hire an LLM developer who will fit in your company culturally.

Test task
Test task

We verify if the candidate really wants to work at your company and is able to spend 5+ hours to prove it by rigorous tests. It helps us to see a developer's caliber.

Technical test
Technical test

Developers are asked LLM related questions and made to solve tricky problems. We use open questions. The goal is not only to test developers’ knowledge – we also want to find out their way of thinking.

Giving specific feedback
Giving specific feedback

We provide explicit feedback on both the test task and the technical test after we have checked the developer's expertise.

What you do

Interview
Interview

You can interview the shortlisted developers to check if the candidate matches your requirements and is a good fit for your company.

Hired/Not-hired
Hired/Not-hired

Hire intelligently with developers sourced by software, vetted by software, matched by software & managed by software.

Top Interview questions to hire LLM developers

Whether you're an IT recruiter or a project manager, you know that finding top developers is critical to the success of your project. Here are some sample interview questions to use when looking to hire LLM developers:

Transfer learning is a technique used in large language models (LLMs) where a pre-trained model is utilized as a starting point for training a new model on a specific task or domain. The idea is that the pre-trained model has already learned a lot of the underlying structure and patterns in the language from the large dataset, and this knowledge can be transferred to the specific task, even if the amount of data for that task is relatively small.

Fine-tuning a pre-trained language model involves adjusting the parameters of an already trained model so that it can adapt to the new, related task. Here are some common techniques used for fine-tuning:

  • Basic Hyper-parameter Tuning: This involves adjusting the hyper-parameters of the model, such as the learning rate, batch size, number of epochs, etc., to improve performance on the specific task. This is often done through a process of trial and error, or using more systematic approaches like grid search or random search.
  • Few-Shot Learning: This is a technique where the model is fine-tuned on a very small amount of data (a "few" examples). The idea is to leverage the knowledge that the model has already learned from pre-training to quickly adapt to new tasks. In the context of language models, this might involve providing a few examples of the task at the beginning of the input, and then asking the model to generate the output for a new example.
  • Data augmentation: Data augmentation techniques can be applied to increase the diversity and size of the labeled dataset. This can involve techniques such as random deletion, swapping, or masking of words in the input text. Data augmentation helps to improve the model's robustness and generalization ability.

Managing bias in large language models involves several steps. First, it's important to use a diverse and representative dataset for training to avoid biases in the data. Second, techniques like fine-tuning on specific tasks can help reduce bias by adapting the model to the task requirements. Third, it's crucial to evaluate the model for bias, using both quantitative metrics and qualitative analysis. Involving a diverse team in the development and review process can also help to identify and mitigate potential biases.

The prominent Python libraries and frameworks used in the development of large language models include TensorFlow, PyTorch, Pandas, NumPy, NLTK, and SQLAlchemy among others.

Evaluating the performance of a large language model typically involves both quantitative and qualitative methods. Quantitative evaluation often includes measuring the model's performance on specific tasks, such as text classification or question answering, using metrics like accuracy, precision, recall, F1 score, or perplexity. Qualitative evaluation involves manually inspecting the model's outputs to assess their quality and relevance. It's also important to evaluate the model for potential biases and fairness issues.

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Frequently Asked Questions

The purpose of the two-week no-risk trial period is to start working with the developers and include them in the team. If you are satisfied with the developers, you keep working with them and pay their salary including the first two weeks. But, if you are not satisfied during the trial period, then you won’t pay anything.

Turing offers top-quality, cost-effective, and highly productive LLM engineers deeply vetted through 50,000+ machine learning signals. Daily standups are mandatory for every Turing developer as they align the developer and the customer with the discussed goal. All Turing remote LLM developers work at least 4 hours in your time zone to ensure effective collaboration.

Turing has created a unique AI-powered deep-vetting talent platform to vet remote developers. Turing tests developers based on actual skills vs. self-reported experience from traditional resumes or job interviews. Every developer at Turing has to clear our tests for programming languages, data structures, algorithms, system designs, software specialization, frameworks, and more. Each Turing developer completes our automated seniority assessment test comprising 57 calibrated questions in 5 areas — project impact, engineering excellence, communication, people, and direction.

To hire high-quality LLM engineers, you must follow these steps:

  • Share your requirements with us
  • Shortlist a few profiles that match your skills requirements
  • Conduct technical and soft skill interviews to finalize your candidates
  • Once selected, you can complete the onboarding process

At Turing, we streamline the entire hiring process for you. We carefully vet top-tier LLM developers worldwide, ensuring they possess exceptional technical and communication skills. Our developers undergo rigorous testing and interviews to guarantee they meet the highest standards. With Turing, you can hire the best LLM talent within 4 days.

Yes, large language model (LLM) engineers are currently in high demand. The development and deployment of LLMs have gained significant attention and traction in various industries and applications. LLMs have shown great potential in natural language understanding, generation, and other language-related tasks, leading to increased demand for professionals with expertise in LLM development.

With Turing, you can hire the best remote developers with 100+ skills such as AI, machine learning, React, Node, Python, Swift, React Native, Android, Java, Rails, Golang, PHP, Vue, DevOps, etc. Turing also offers developers based on tech stack and seniority.

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