1 to 25 of 403 TensorFlow Jobs

science lifecycle. Expertise taking projects from ideation or experimental Jupyter notebooks to full production deployment. Strong programming skills in Python, with familiarity in ML libraries/frameworks such as TensorFlow, PyTorch, and Scikit-learn. Experience with MLOps practices including model drift detection, decay, A/B testing, integration testing, differential testing, Python package building, and code version control. Skilled More ❯

related field. 7+ years of professional software development experience, with at least 3 years in AI/ML. Strong proficiency in Python , including libraries like NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch . Solid understanding of ML algorithms , NLP , deep learning , and statistical methods. Experience with Docker, Kubernetes , and cloud platforms like AWS/Azure/GCP . Hands-on More ❯

in software engineering and machine learning/AI engineering roles (building, deploying, operating machine learning models and AI systemsExpertise in programming languages such as Python, and framework experience (e.g., TensorFlow, PyTorch, scikit-learn, KerasProven track record in designing, building, deploying production-grade ML/AI systems in real-world settings.Deep experience in feature engineering, model development, tuning, validation, and More ❯

Data Science , or a related technical field. Strong understanding of core ML concepts (supervised/unsupervised learning, neural networks, NLP, etc.). Hands-on experience with frameworks such as TensorFlow , PyTorch , or scikit-learn . Proficiency in Python and familiarity with common data science libraries (NumPy, pandas, etc.). Solid grasp of statistics, linear algebra, and probability. Excellent problem More ❯

with predictive modeling techniques such as regression, classification, clustering, or time-series forecasting. Proficiency in Python and experience with data science libraries (e.g., Pandas, NumPy, scikit-learn, XGBoost, PyTorch, TensorFlow). Strong experience with SQL and data manipulation across large datasets. Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Plotly, Tableau, or Power BI). Exposure to modern collaborative More ❯

ideation to delivery, including business scoping and stakeholder management. Strong proficiency in Python (or R), with deep experience using modern data science libraries (e.g., Pandas, NumPy, scikit-learn, PyTorch, TensorFlow, Statsmodels). Solid foundation in SQL and data wrangling across large, complex datasets. Hands-on experience with experimentation platforms, data visualization, and dashboarding tools (e.g., Tableau, Power BI, Plotly More ❯

science. Requirements 5+ years of experience in data science, machine learning, or AI model development. Expertise in Python, R, or Julia, with proficiency in pandas, NumPy, SciPy, scikit-learn, TensorFlow, or PyTorch. Experience with SQL, NoSQL, and big data technologies (Spark, Hadoop, Snowflake, Databricks, etc.). Strong background in statistical modelling, probability theory, and mathematical optimization. Experience deploying machine More ❯

or platform scale. Proven track record of leading ML engineering projects from architecture to deployment, including ownership of production-grade systems. Deep expertise in ML frameworks and engineering stacks (TensorFlow, PyTorch, JAX, Ray, MLflow, Kubeflow). Proficiency in Python and at least one backend language (e.g., Java, Scala, Go, C++). Strong understanding of cloud ML infrastructure (AWS SageMaker More ❯

Agile methodologies.Excellent analytical, problem-solving, and communication skills.Preferred SkillsExperience with data engineering, ETL workflows, or big data frameworks (Spark, Airflow).Knowledge of machine learning libraries (NumPy, Pandas, Scikit-learn, TensorFlow, etc.) is a plus.Exposure to DevOps practices, infrastructure as code, and monitoring tools (Jenkins, Terraform, Prometheus).Familiarity with security best practices for Python-based applications.Prior experience in domains such More ❯

in developing and deploying AI/ML solutions, with a strong focus on Microsoft Azure. • Proficiency in Python and experience with relevant AI/ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, Keras). • Demonstrated experience with Azure Machine Learning, including MLOps concepts, model registration, deployment (AKS, ACI), and monitoring. • Hands-on experience with Azure data services such as Azure More ❯

in developing and deploying AI/ML solutions, with a strong focus on Microsoft Azure. • Proficiency in Python and experience with relevant AI/ML libraries (e.g., scikit-learn, TensorFlow, PyTorch, Keras). • Demonstrated experience with Azure Machine Learning, including MLOps concepts, model registration, deployment (AKS, ACI), and monitoring. • Hands-on experience with Azure data services such as Azure More ❯

with a strong portfolio of high-impact projects in production Expert-level programming skills in Python and SQL, and fluency with leading ML/AI frameworks (e.g., scikit-learn, TensorFlow, PyTorch) Direct experience with GenAI/LLM technologies, including tools like Hugging Face, LangChain, OpenAI APIs, vector databases, and fine-tuning methods Deep knowledge of machine learning algorithms (supervised More ❯

Engineering, or related field. - 3+ years in ML, DevOps, or MLOps with hands-on experience deploying ML products. - Python, Go, Rust, or similar programming languages - ML/DL frameworks (TensorFlow, PyTorch, scikit-learn, etc.) - DevOps practices, CI/CD, IaC, Docker, Kubernetes - Version control (Git) and collaborative development tools - Data engineering and ETL workflows - Monitoring/logging tools (Prometheus More ❯

techniques. Demonstrated ability to implement, validate, and improve supervised and unsupervised learning models. Excellent knowledge of Excel, Python, SQL, and database systems. Familiarity with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, or related frameworks. Experience in evaluation metrics, model selection, and validation techniques such as cross-validation, bootstrapping, and assessing the statistical significance of model improvements. Excellent More ❯

or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field (or equivalent experience). Technical Skills: Proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch). Strong understanding of NLP techniques, including tokenization, embeddings, transformers, and attention mechanisms. Experience in retraining and fine-tuning LLMs using large-scale datasets. Familiarity with cloud platforms More ❯

emerging GenAI applications. The work you could be doing • Design and deploy machine learning models for fraud detection, credit risk, customer segmentation, and behavioural analytics using scalable frameworks like TensorFlow, PyTorch, and XGBoost. • Engineer robust data pipelines and ML workflows using Apache Spark, Vertex AI, and CI/CD tooling to ensure seamless model delivery and monitoring. • Apply advanced More ❯

methodologies.Excellent analytical, problem-solving, and communication skills. Preferred Skills Experience with data engineering, ETL workflows, or big data frameworks (Spark, AirflowKnowledge of machine learning libraries (NumPy, Pandas, Scikit-learn, TensorFlow, etc is a plus.Exposure to DevOps practices, infrastructure as code, and monitoring tools (Jenkins, Terraform, PrometheusFamiliarity with security best practices for Python-based applications.Prior experience in domains such as More ❯

environments. Collaborate across engineering, security, and product teams to deliver at pace and scale. The toolkit you’ll use 🌳 Data Science & Engineering: Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow), SQL, NoSQL, Spark, big data ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud: CI/CD, Docker, AWS (S3, Lambda More ❯

environments. Collaborate across engineering, security, and product teams to deliver at pace and scale. The toolkit you’ll use 🌳 Data Science & Engineering: Python (NumPy, Pandas, scikit-learn, PyTorch/TensorFlow), SQL, NoSQL, Spark, big data ecosystems Visualisation & APIs: REST/JSON, Postman, Flask/FastAPI, Power BI/Tableau, D3.js DevOps & Cloud: CI/CD, Docker, AWS (S3, Lambda More ❯

integrations, asyncio for concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning More ❯

integrations, asyncio for concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning More ❯

integrations, asyncio for concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks- Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning More ❯

integrations, asyncio for concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks- Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning More ❯

years of focused experience in AI and data-driven architecture design. • Extensive experience in enterprise architecture frameworks (e.g., TOGAF, Zachman). • Expertise in AI/ML technologies (e.g., TensorFlow, PyTorch, Scikit-learn, Keras) and understanding of deep learning and natural language processing (NLP). • Strong understanding of big data platforms such as Hadoop, Apache Spark, and data lakes. • Hands More ❯

of experience in data science roles, with proven expertise in: Machine learning (supervised/unsupervised), time series forecasting, deep learning. Programming in Python and SQL; proficiency with ML frameworks (TensorFlow, PyTorch, scikit-learn). Big data technologies and cloud platforms (AWS, Azure, or GCP). Track record of deploying models into production and measuring business impact. Strong problem-solving More ❯