What Deep Learning Engineers Deliver in AI Model Development
Does your product use a voice assistant, surface smart recommendations, or flag a fraud transaction in real time? Well, it is deep learning doing all the heavy lifting.
Hire Top Deep Learning Engineers - Shortlisted by AI
Recently Added Deep Learning Engineers in our Network
Lalkrishna Arjun
Machine Learning & Deep learning Engineer3 Years of Exp- machine_learning
- Micro services
- model optimization
- Apache TVM
- View all (9)
Nevil Shah
Sr Software MLOps & Deep learning engineer6 Years of Exp- Classification
- Cnn
- LLMs
- ML libraries
- Regression
- Scikit-learn
- View all (9)
Sheetal Jantikar
Software Development Engineer & Deep learning engineer7 Years of Exp- Python
- 2D
- Java
- C
- SQL
- SNS
- Apache Flink
- AWS S3
- C++
- Docker
- DynamoDB
- View all (14)
Saurabh Jain
Senior Software & Deep learning engineer11 Years of Exp- Django or Flask
- MySQL or PostgreSQL
- Oop
- RESTful API
- Python
- AI
- ML
- View all (9)
Parth Bhatnagar
Data Science Researcher & Deep learning engineer2 Years of Exp- Apache-spark
- AWS
- Big Data
- Bootstrap
- C
- C++
- Computer Vision
- CSS
- View all (10)
Shreyas Somashekar
AI / ML & Deep learning engineer6 Years of Exp- Python
- Ai deployment
- AI models
- Deep Learning
- machine_learning
- View all (9)
India's largest network of 3M+ professionals
Check out some of the candidates who recently joined.
Hire Deep Learning Engineers in 4 Easy Steps
01
Define
Tell us what you need
You define the role, we match immediately.
02
Discover
Meet the top talent
Get 3 to 5 highly relevant candidates in 48 hours.
03
Evaluate
Interview with ease
Choose the candidate that aligns with your needs and we'll arrange an interview.
04
Onboard
Hire with confidence
Once you decide, we'll take care of the onboarding process for you.
Top Reasons to Choose Uplers
Hire in 48 Hours
Receive the top 3-5 AI-interviewed profiles from our network within 2 days.
Top 3.5% Talents
Only the best profiles vetted using AI and human intelligence make it to your inbox.
Start-up ready Matching
Engineers who wear multiple hats, move fast, and don't need hand-holding.
Works in 5+ Time Zones
Engineers overlap with EST/PST: 4–6 hours daily and flexible to preferred time zones.
Employer on Record (EOR)
We handle all legal and payroll complexity of hiring from India, so you don't have to.
Simple Contracts
Straightforward agreement with top-most flexibility and freedom.
30 Days Cancellation
Cancel without any obligations in cases of dissatisfaction, financial instability, or business slowdown.
2X Retention Rate
92% of placed engineers still with clients after 12 months
Various Skills that Deep Learning Engineers Possess
Access the talent network of 3M+ professionals with 100+ skill sets
Deep learning
Machine learning
Neural networks
Artificial neural networks (ANN)
Convolutional neural networks (CNN)
Recurrent neural networks (RNN)
LSTM and GRU networks
Transformers
Attention mechanisms
Generative models
GANs (Generative Adversarial Networks)
Autoencoders
Diffusion models
Representation learning
Transfer learning
Self supervised learning
Reinforcement learning basics
Model architecture design
Model training and evaluation
Hyperparameter tuning
Model optimization
Loss functions and activation functions
Backpropagation
Gradient descent optimization
Feature engineering
Data preprocessing
Data augmentation
Large dataset handling
Python
C++
Deep learning
Machine learning
Neural networks
Artificial neural networks (ANN)
Convolutional neural networks (CNN)
Recurrent neural networks (RNN)
LSTM and GRU networks
Transformers
Attention mechanisms
Generative models
GANs (Generative Adversarial Networks)
Autoencoders
Diffusion models
Representation learning
Transfer learning
Self supervised learning
Reinforcement learning basics
Model architecture design
Model training and evaluation
Hyperparameter tuning
Model optimization
Loss functions and activation functions
Backpropagation
Gradient descent optimization
Feature engineering
Data preprocessing
Data augmentation
Large dataset handling
Python
C++
Deep learning
Machine learning
Neural networks
Artificial neural networks (ANN)
Convolutional neural networks (CNN)
Recurrent neural networks (RNN)
LSTM and GRU networks
Transformers
Attention mechanisms
Generative models
GANs (Generative Adversarial Networks)
Autoencoders
Diffusion models
Representation learning
Transfer learning
Self supervised learning
Reinforcement learning basics
Model architecture design
Model training and evaluation
Hyperparameter tuning
Model optimization
Loss functions and activation functions
Backpropagation
Gradient descent optimization
Feature engineering
Data preprocessing
Data augmentation
Large dataset handling
Python
C++
Java
R
Julia
TensorFlow
PyTorch
Keras
JAX
MXNet
Caffe
OpenCV
NumPy
Pandas
SciPy
Scikit learn
Hugging Face Transformers
LangChain
DeepSpeed
ONNX
TensorRT
CUDA
GPU computing
Distributed training
Parallel computing
MLflow
Kubeflow
Weights and Biases
Neptune.ai
Docker
Kubernetes
CI/CD for ML
Java
R
Julia
TensorFlow
PyTorch
Keras
JAX
MXNet
Caffe
OpenCV
NumPy
Pandas
SciPy
Scikit learn
Hugging Face Transformers
LangChain
DeepSpeed
ONNX
TensorRT
CUDA
GPU computing
Distributed training
Parallel computing
MLflow
Kubeflow
Weights and Biases
Neptune.ai
Docker
Kubernetes
CI/CD for ML
Java
R
Julia
TensorFlow
PyTorch
Keras
JAX
MXNet
Caffe
OpenCV
NumPy
Pandas
SciPy
Scikit learn
Hugging Face Transformers
LangChain
DeepSpeed
ONNX
TensorRT
CUDA
GPU computing
Distributed training
Parallel computing
MLflow
Kubeflow
Weights and Biases
Neptune.ai
Docker
Kubernetes
CI/CD for ML
Linux
Git
Version control systems
REST APIs
Linear algebra
Calculus
Probability and statistics
Optimization techniques
Computer vision
Natural language processing
Speech recognition
Recommendation systems
Time series modeling
Model deployment
Model monitoring
MLOps practices
Pipeline automation
Performance optimization
Scalability engineering
Data analysis
Algorithm development
Research methodology
Technical documentation
Analytical thinking
Problem solving
Critical thinking
Attention to detail
Experimentation mindset
Collaboration with data science and engineering teams
Communication skills
Linux
Git
Version control systems
REST APIs
Linear algebra
Calculus
Probability and statistics
Optimization techniques
Computer vision
Natural language processing
Speech recognition
Recommendation systems
Time series modeling
Model deployment
Model monitoring
MLOps practices
Pipeline automation
Performance optimization
Scalability engineering
Data analysis
Algorithm development
Research methodology
Technical documentation
Analytical thinking
Problem solving
Critical thinking
Attention to detail
Experimentation mindset
Collaboration with data science and engineering teams
Communication skills
Linux
Git
Version control systems
REST APIs
Linear algebra
Calculus
Probability and statistics
Optimization techniques
Computer vision
Natural language processing
Speech recognition
Recommendation systems
Time series modeling
Model deployment
Model monitoring
MLOps practices
Pipeline automation
Performance optimization
Scalability engineering
Data analysis
Algorithm development
Research methodology
Technical documentation
Analytical thinking
Problem solving
Critical thinking
Attention to detail
Experimentation mindset
Collaboration with data science and engineering teams
Communication skills
Begin your hiring journey with us!
Hire a top talentTop Clients Reviews
Uplers earned our trust by listening to our problems and finding the perfect talent for our organization.
Uplers helped to source and bring out the top talent in India, any kind of high-level role requirement in terms of skills is always sourced based on the job description we share. The profiles of highly vetted experts were received within a couple of days. It has been credible in terms of scaling our team out of India.
Uplers efficient, quick process and targeted approach helped us find the right talents quickly. The professionals they provided were not only skilled but also a great fit for our team.
Uplers' talents consistently deliver high-quality work along with unmatched reliability, work ethic, and dedication to the job.
Case Studies of Tech Companies
Check Our Latest Blogs
Frequently Asked Questions
Uplers provides AI-vetted talent, ensuring a seamless hiring experience. Our efficient process ensures profile shortlisting within 48 hours, allowing you to swiftly onboard qualified professionals within just 2 weeks. Additionally, we prioritize client satisfaction with our flexible terms, including a 30-day cancellation policy and a lifetime free replacement.
You can get the top 3.5% of AI-vetted profiles in less than 48 hours through Uplers. Once you finalize one of the most suitable Deep Learning Engineers, Uplers takes care of the entire hiring and onboarding formalities. This typically takes 2-4 weeks depending on your requirements and decision-making time.
The modes of communication through which you can get in touch with a hired Deep Learning Engineers include:
- Phone
- Messaging apps such as WhatsApp, Slack, or Microsoft Teams
Uplers offers a 30-day cancellation policy at no extra cost and lifetime free replacement.
The average cost of hiring a Deep Learning Engineers from Uplers starts at $2500. The number varies depending on the experience level of the developer as well as your requirements.
At Uplers, our screening process ensures a thorough evaluation of candidates' language proficiency, facilitated by our AI-vetting technology. Beyond linguistic skills, we prioritize cultural fitness to ensure seamless integration within your team, fostering a harmonious work environment and seamless collaboration.
A Deep Learning engineer designs and trains neural network models that can recognize patterns, learn from large datasets, and make accurate predictions. This expertise helps businesses build advanced AI systems for applications such as image recognition, natural language processing, recommendation engines, and intelligent automation
A hiring manager should look for expertise in deep learning frameworks such as TensorFlow or PyTorch, strong knowledge of neural networks, and solid programming skills in Python. Experience with data preprocessing, model training, optimization, and working with large datasets is also important for building reliable AI models.
Neural networks for computer vision, NLP, and speech recognition require expertise in model architecture, data preparation, and training techniques. A Deep Learning engineer helps design, train, and optimize these models to accurately analyze images, understand language, and process speech, enabling solutions such as image recognition systems, chatbots, translation tools, and voice assistants.
Designing and training large-scale deep learning models requires expertise in neural network architecture, data handling, and model optimization. A Deep Learning engineer builds and trains these models using large datasets, improves model performance, and ensures scalable deployment for real-world AI applications.
Model accuracy, scalability, and performance depend on proper data preparation, model architecture, and optimization techniques. A Deep Learning engineer improves model accuracy through training and evaluation, optimizes algorithms for faster performance, and builds scalable systems that can handle large datasets and real-world workloads.
Yes. A Deep Learning engineer prepares trained models for real-world use by optimizing performance, integrating models with applications, and deploying them on cloud or server environments. This process ensures AI models run reliably, scale efficiently, and deliver accurate results in production systems.
Strong experience with frameworks like TensorFlow, PyTorch, or Keras is essential for building and training deep learning models. A Deep Learning engineer should know how to design neural networks, train models on large datasets, optimize performance, and evaluate model accuracy using these frameworks.
Data preprocessing involves cleaning, labeling, and transforming raw data into a format suitable for training. A Deep Learning engineer prepares datasets, trains neural network models using large data samples, and adjusts hyperparameters such as learning rate, batch size, and model layers to improve accuracy and performance.
Collaboration involves aligning AI models with business goals and product requirements. A Deep Learning engineer works with data scientists to prepare datasets and design models, partners with ML engineers to optimize and deploy models, and coordinates with product teams to ensure AI solutions solve real user problems.
A company should hire a Deep Learning engineer when projects require complex neural networks, large-scale data processing, or advanced AI applications such as computer vision, natural language processing, or speech recognition. Specialized expertise helps design, train, and optimize deep learning models for higher accuracy and scalable performance.
