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Deep Learning Ai Jobs in Spring, TX (NOW HIRING)

Gainwell Technologies LLC

Expert AI Engineer

Houston, TX · On-site

$147K - $210K/yr

AI Model Development - Design, build, and train machine learning and deep learning models, including GenAI, NLP, and predictive analytics solutions for healthcare applications. * End-to-End AI ...

Marencor

Gen AI/ML Solution Architect

Houston, TX · On-site

$60.25 - $79.25/hr

... mining, deep learning, predictive analytics, and machine learning. * Proven track record in the full data science project lifecycle, including data wrangling, statistical analysis, and data ...

Bespoke Labs

Machine Learning Engineer

Houston, TX · On-site

About Us We are AI researchers and builders who understand how to curate data and RL environments ... deep learning proficiency (PyTorch preferred; familiar with training loops, optimizers, mixed ...

Augment

Principal Scientist, Data

Houston, TX

$80 - $83/hr

Key Responsibilities AI/ML Model Development * Design, train, fine-tune, and evaluate machine learning and deep learning models--including LLMs--for predictive analytics and automated decision-making.

Augment

Principal Scientists, Data

Houston, TX

Required Skills & ExperienceTechnical ExpertiseHands-on experience with AI/ML, NLP, LLM applications (GPT/BERT/LangChain), predictive modeling, and deep learning.Strong proficiency in Python and ML ...

Cisco

Applied AI Scientist

Houston, TX

We combine deep AI research expertise with the scale and operational excellence of Splunk and Cisco ... Deep experience with graph representation learning, graph transformers (e.g., GCN/GAT/GraphSAGE ...

Deloitte

AI Solutions Architect

Houston, TX · On-site

$60.25 - $79.25/hr

... deep learning, or generative artificial intelligence solutions in production environments ... Overseeing execution of AI-powered solutions, including risk identification, ethical AI ...

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All JobsDeep Learning Ai JobsDeep Learning Ai Jobs in Spring, TX

Deep Learning Ai information

See Spring, TX salary details

$9.8K

$74.6K

$124.6K

How much do deep learning ai jobs pay per year?

As of Jul 13, 2026, the average yearly pay for deep learning ai in Spring, TX is $74,649.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,100.00 and $123,700.00 per year, depending on experience, location, and employer.

What is the difference between Deep Learning Ai vs Machine Learning Engineer?

AspectDeep Learning AiMachine Learning Engineer
Required CredentialsDegree in Computer Science, Data Science, or related fields; knowledge of neural networksDegree in Computer Science, Data Science, or related fields; programming skills in Python, R
Work EnvironmentResearch labs, AI development teams, tech companies focusing on AI modelsSoftware development teams, data analysis projects across various industries
Industry UsagePrimarily in AI research, autonomous systems, NLP, computer visionAcross industries for predictive modeling, data analysis, automation

Deep Learning Ai specialists focus on designing and implementing neural network models for complex AI tasks, often requiring advanced knowledge of deep neural networks. Machine Learning Engineers develop broader machine learning models, including traditional algorithms. While both roles require similar educational backgrounds, Deep Learning Ai roles are more specialized in neural networks and AI research, whereas Machine Learning Engineers work across a wider range of algorithms and applications.

What is the main job of deep learning in AI?

The main job of deep learning in AI is to develop models that can automatically learn complex patterns and representations from large amounts of data, enabling tasks such as image recognition, natural language processing, and speech understanding. Deep learning engineers design, train, and optimize neural networks using tools like TensorFlow or PyTorch to improve AI system performance.

What are some common challenges faced by professionals working in Deep Learning AI, and how can they be addressed?

Professionals in Deep Learning AI often encounter challenges such as managing large datasets, ensuring model accuracy, and addressing issues like overfitting. Collaboration with data engineers and domain experts is crucial to ensure high-quality data and relevant feature selection. Additionally, staying up-to-date with rapidly evolving frameworks and algorithms requires continuous learning and participation in knowledge-sharing within the team. Regular code reviews and experimentation with different architectures can help overcome technical obstacles and improve model performance.

What is the salary of AI and deep learning?

The salary for roles in AI and deep learning varies based on experience, location, and education, but typically ranges from $80,000 to over $150,000 annually for skilled professionals. Entry-level positions may start around $70,000, while senior roles or those requiring advanced skills in machine learning frameworks and programming languages can earn higher salaries.

What are the key skills and qualifications needed to thrive as a Deep Learning AI Engineer, and why are they important?

To thrive as a Deep Learning AI Engineer, you need a strong background in mathematics, programming (especially Python), and experience with neural networks, typically supported by a degree in computer science, engineering, or a related field. Proficiency with deep learning frameworks such as TensorFlow or PyTorch, and knowledge of tools like CUDA for GPU acceleration, are essential; relevant certifications can be advantageous. Analytical thinking, creativity, and effective communication are important soft skills for solving complex problems and collaborating with cross-functional teams. These skills and qualities are crucial for building robust AI models and driving innovation in this rapidly evolving field.

Which 3 jobs will survive AI?

Deep Learning AI professionals will continue to find roles in research, model development, and AI ethics, as these areas require specialized expertise and human oversight. Jobs involving complex problem-solving, creativity, and emotional intelligence, such as AI research scientists, data scientists, and AI ethics specialists, are less likely to be fully automated. Skills in programming, data analysis, and understanding of AI frameworks will remain valuable in these roles.

What are Deep Learning AI professionals?

Deep Learning AI professionals are experts who design, develop, and implement artificial intelligence systems that use deep neural networks to analyze complex data and solve tasks such as image recognition, natural language processing, and autonomous decision-making. They work with large datasets and advanced algorithms to build models that can learn and improve over time. These professionals often have a background in computer science, mathematics, or engineering, and are skilled in programming languages like Python and frameworks such as TensorFlow or PyTorch.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as a senior AI researcher, machine learning director, or AI executive, often requiring advanced skills, extensive experience, and leadership responsibilities. These roles may involve overseeing AI projects, developing innovative algorithms, and managing teams, with compensation reflecting the expertise and impact of the position.
What are popular job titles related to Deep Learning Ai jobs in Spring, TX? For Deep Learning Ai jobs in Spring, TX, the most frequently searched job titles are:
What job categories do people searching Deep Learning Ai jobs in Spring, TX look for? The top searched job categories for Deep Learning Ai jobs in Spring, TX are:
What cities near Spring, TX are hiring for Deep Learning Ai jobs? Cities near Spring, TX with the most Deep Learning Ai job openings:
Deep Learning Ai jobs near you

Senior AI Engineer

Goldenpick Technologies

Houston, TX • On-site

$99K - $137K/yr

Contractor

Re-posted 5 days ago

Job description

Project description
We are seeking a highly skilled Senior AI Engineer to design, build, and deploy scalable artificial intelligence and machine learning solutions. This role requires strong expertise in developing predictive models, implementing AI-powered applications, and integrating them into enterprise systems. The ideal candidate combines deep technical proficiency with practical experience in cloud platforms and production-grade AI systems.
 
Responsibilities
  • AI/ML Model Development
  • Design, develop, and optimize machine learning and deep learning models
  • Build NLP, computer vision, or predictive analytics solutions
  • Train, test, and evaluate models for accuracy, scalability, and performance
  • Fine-tune pre-trained models (e.g., LLMs, transformers) for business use cases
  • Data Engineering & Processing (Optional)
  • Collect, clean, and preprocess structured and unstructured datasets
  • Work with large-scale data pipelines and streaming data systems
  • Implement feature engineering and data transformation workflows
  • Deployment & MLOps
  • Deploy models into production using APIs, containers, or microservices
  • Work with DevOps to build CI/CD pipelines for ML workflows (MLOps)
  • Monitor model performance, drift, and reliability in production
  • Optimize latency, throughput, and cost efficiency
  • Cloud & System Integration
  • Integrate AI solutions into cloud platforms (AWS)
  • Work with services like SageMaker, Azure ML, Vertex AI, or OpenAI APIs
  • Collaborate with DevOps, backend, and frontend teams for implementation
  • Research & Innovation
  • Stay up to date with emerging AI technologies and frameworks
  • Evaluate and implement GenAI, LLMs, and prompt engineering techniques
  • Prototype and experiment with new AI-driven solutions
 
Skills Must have
  • Programming: Python (preferred), Java, or Scala
  • ML Frameworks: TensorFlow, PyTorch, Scikit-learn
  • AI/GenAI: prompt engineering (preferred), Claude CLI / Code(preferred), LLMs(preferred) Hugging Face, OpenAI APIs
  • Data Tools (Optional): Pandas, NumPy, Spark
  • APIs & Microservices development
  • Version control (Git)
  • Cloud & DevOps
  • Experience with AWS
  • Containers: Docker, ECS/EKS
  • CI/CD pipelines (GitHub Actions Or Jenkins Or GitLab CI)
  • Data & Systems
  • Databases: SQL, NoSQL
  • Familiarity with data pipelines and ETL processes (Optional)
  • Understanding of distributed systems