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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
Goutham

Goutham

LLM Developer

Experience7 years
AvailabilityFull-time

Goutham is an ML engineer with 7+ years' experience shaping and deploying AI solutions. He is in proficient in Python, Keras, and scikit-learn, with expertise in LLM-driven model creation.

Expert in
  • LLM
  • NLP
  • Machine Learning
  • Computer Vision
  • SQL
Also worked with
  • Git
  • Keras
  • PyTorch
  • Matplotlib
Hoang

Hoang

AI Engineer

Experience3 years
AvailabilityFull-time

Hoang is an AI/ML/SWE enthusiast who has 3+ years of experience working in startups and large corporations. He has performed AI/ML roles at Microsoft, SoftBank, and Fujitsu.

Expert in
  • AI
  • HTML
  • Tensorflow
  • A/B Testing
  • Git
Also worked with
  • Java
  • Neo4j
  • JavaScript
  • Python
  • SQL
Nitin

Nitin

NLP Engineer

Experience4 years
AvailabilityFull-time

Nitin is a senior engineer with 4+ years of experience in technologies like Natural Language Processing (NLP), Data Analytics, and Artificial Intelligence/Machine Learning.

Expert in
  • NLP
  • Machine Learning
  • Github
  • MySQL
Also worked with
  • Data Science
  • Data Analysis
  • ETL
Nishat

Nishat

CV Engineer

Experience4 years
AvailabilityFull-time

Nishat is a data analyst with 4+ years of experience in Machine and Deep Learning for computer vision and NLP use cases. She is also skilled in Python, Spark, Docker, Airflow, etc.

Expert in
  • CV
  • Python
  • Spark
  • Docker
  • Airflow
Also worked with
  • AI
  • NLP
  • Django
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Guide to hiring remote generative AI developers

Hire generative AI 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 generative AI developers
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Hiring generative AI developers: Everything you need to know

Generative AI is a rapidly evolving tech that has revolutionized how we approach problem-solving and innovation. By leveraging advanced algorithms and machine learning techniques, generative AI enables systems to autonomously generate new and original content, such as images, music, text, and videos. Due to GenAI’s rapidly expanding impact and use cases, the demand for generative AI developers has gone up significantly.

Who is a generative AI developer?

A generative AI developer is a skilled professional specializing in developing and implementing generative artificial intelligence systems. These developers deeply understand machine learning algorithms, neural networks, and statistical modeling techniques. They are proficient in programming languages, libraries, and frameworks such as Python, TensorFlow, and PyTorch, commonly used in building generative AI models.

Why should you hire generative AI developers?

Generative AI developers are highly proficient candidates with a unique skill set that can help propel businesses to new growth dimensions and innovation. The prominent reasons why you should hire generative AI developers are as follows:

  • Innovation and creativity Generative AI developers can create innovative solutions to revolutionize business operations. They can design AI systems that generate new ideas, content, or solutions, enhancing creativity and innovation.
  • Automation Generative AI systems can automate repetitive tasks, allowing your team to focus on more strategic initiatives. This can significantly improve the efficiency and productivity of your business.
  • Data analytics systems Generative AI developers can build systems to analyze large amounts of data and generate insights. These systems can help you make more informed business decisions.
  • Personalized customer experience Generative AI can help create personalized customer experiences. This feature can improve customer satisfaction and loyalty, increasing sales and revenue.
  • Scalability Generative AI models can scale with your business needs. As your operations grow, these models can adapt and handle increased workloads without extensive manual intervention.

In conclusion, hiring generative AI developers can empower your business to unlock the full potential of AI-driven innovation.

Benefits of hiring Turing generative AI developers

Turing offers deeply vetted, highly experienced GenAI engineers with expertise in neural networks, machine learning algorithms, statistical modeling, Python programming, data processing and augmentation, deep learning frameworks, and other relevant skills. We also vet generative AI developers for communication and seniority skills to ensure they integrate seamlessly with your team.

When you hire generative AI developers from Turing, you get the following benefits:

  • Access to global developer pool
  • Rigorously vetted developers
  • Hiring 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 generative AI developers.

Roles and responsibilities of generative AI developers

The prominent roles and responsibilities of generative AI developers are as follows:

  • Generative AI model development Generative AI developers are primarily responsible for designing, developing, and implementing AI systems that generate relevant solutions based on specific business needs and use cases.
  • Data management GenAI developers are responsible for managing and interpreting complex datasets. It involves preprocessing the data, selecting the appropriate features, and using this data to train and refine AI models.
  • Evaluation and validation Generative AI developers evaluate and validate the performance and quality of generative AI models through rigorous testing and benchmarking to ensure the generative AI models offer satisfactory performance relevant to the business.
  • AI model integration Generative AI developers are responsible for integrating generative AI models into existing software applications to augment the functionality of existing systems or applications and developing standalone generative AI tools.
  • Collaboration Generative AI developers collaborate with cross-functional teams, including data scientists, software engineers, and domain experts, to understand project requirements and align work with organizational objectives.

Skills to look for when hiring generative AI developers

Here are some critical skills to consider when hiring GenAI developers:

1. Strong background in machine learning

Candidates should have a solid understanding of machine learning concepts, algorithms, and techniques. This includes knowledge of supervised and unsupervised learning and experience with deep learning frameworks and architectures.

2. Experience with generative models

Candidates should have hands-on experience with generative models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), or other relevant architectures. They should demonstrate their ability to effectively design, train, and evaluate generative models.

3. Proficiency in programming languages

Look for candidates proficient in programming languages used in generative AI development, such as Python. They should also be familiar with relevant libraries and frameworks like TensorFlow, PyTorch, or Keras.

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4. Familiarity with data preprocessing and augmentation

Candidates should be skilled in preparing and preprocessing datasets for training generative models. This includes techniques like data cleaning, normalization, and augmentation to ensure the quality and diversity of the training data.

5. Strong analytical and research skills

The developers should be able to analyze and interpret results, evaluate model performance, and iterate on their approaches based on evolving evidence. They should also be able to stay updated with the latest research papers and advancements in the field.

6. Collaboration and communication skills

Effective collaboration and communication skills are essential for working in cross-functional teams and effectively conveying ideas and results. Look for candidates who can articulate complex concepts and work well with others.

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 generative AI 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 Angular 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 Angular 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 generative AI developers

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

Loss functions in generative AI serve as critical optimization parameters that quantify the discrepancy between the generated output and the target data. They guide the learning process by measuring the performance of the generative model. By minimizing the loss function during training, the model iteratively adjusts its parameters to generate data that closely resembles the desired output, be it images, text, or other forms of content.

Latent space refers to a lower-dimensional, abstract representation of data learned by the model during training. It captures essential features or attributes of the data in a compact form. In the context of generative models like Variational Autoencoders (VAEs), the latent space is a critical component. For VAEs, the latent space is often represented as a probability distribution (e.g., Gaussian distribution) and is used for encoding and decoding data. It enables the generation of new data points by sampling from this distribution.

Transfer learning is a technique in machine learning where knowledge gained from training one model on a specific task is transferred and applied to another related task. In the context of generative AI, transfer learning can be used to leverage pre-trained models on large datasets or complex tasks and apply them to new generative tasks with limited data or resources. By utilizing the learned representations and weights from the pre-trained model, generative AI developers can accelerate training, improve performance, and better generalize the target task.

Attention mechanisms in transformer-based models enable the models to focus on relevant parts of the input sequence when generating an output. Instead of relying solely on fixed-length context windows, attention mechanisms allow the model to dynamically weigh the importance of different positions in the input sequence. This is achieved by computing attention scores between each position and all other positions in the sequence. These scores determine the contribution of each position to the final output. By attending to different parts of the input sequence, attention mechanisms enable transformer-based models to capture long-range dependencies and improve their ability to generate accurate and contextually relevant outputs.

Several techniques are utilized to address overfitting when training generative models, including increasing dataset size, regularization, data augmentation, cross-validation, and regularizing the loss function, among other techniques.

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Frequently Asked Questions

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

Turing offers top-quality, cost-effective, and highly productive generative AI developers 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 generative AI 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 generative AI developers, 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 generative AI 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 generative AI talent within 4 days.

Yes, generative AI developers are in high demand. The field of generative AI has gained significant attention and popularity in recent years due to its potential to create realistic and creative outputs. Many industries, including software, entertainment, gaming, advertising, fashion, marketing, and healthcare, are actively exploring the applications of generative AI. As a result, there is a growing need for skilled generative AI developers who can design, develop, and deploy generative models to meet the demands of these industries.

With Turing, you can hire the best remote developers with 100+ skills such as 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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