1 to 25 of 383 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 ❯

on experience in developing and deploying machine learning models in a production environment. Strong programming skills in Python and experience with relevant machine learning libraries and frameworks such as TensorFlow, Keras, PyTorch, scikit-learn, etc. Solid understanding of machine learning algorithms (e.g., regression, classification, clusting, dimensionality reduction, deep learning architectures). Experience with data preprocessing, feature engineering, and data More ❯

hands-on experience with a wide range of ML techniques (supervised/unsupervised learning, time-series analysis, ensemble methods). Advanced AI: Practical experience with Deep Learning frameworks (e.g., TensorFlow, PyTorch) for applications like NLP (Transformer models, BERT) or computer vision. Big Data Tools: Experience with big data platforms like Spark (PySpark) for handling large-scale datasets. MLOps: Familiarity 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 ❯

robust, scalable ML infrastructure, internal libraries, and reusable components that enhance team productivity Proficiency in Python and experience with ML libraries (scikit-learn, pandas, numpy) and deep learning frameworks (TensorFlow, PyTorch, Keras) Advanced hands-on experience with cloud platforms (AWS, GCP, Azure) and implementing production-grade MLOps pipelines, containerization (Docker/Kubernetes), and automated deployment systems Experience with version More ❯

degree in a STEM field: Statistics, Mathematics, Computer Science, Engineering, or similar. Programming proficiency: Strong experience with Python and SQL; familiarity with libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch. Machine learning expertise: Hands-on experience in supervised and unsupervised learning, with exposure to NLP, computer vision, or deep learning a plus. Data visualisation: Proficiency in Tableau More ❯

Spiral, Agile, Waterfall, Lean. Programming Language: C++, Python, Shell, SQL, R. IDE Tools: PyCharm, RStudio, Visual Studio Code, Jupyter Notebook, Google Collab, Navicat. ML Frameworks: Transformers, Scikit-Learn, Keras, TensorFlow, PyTorch, ONNX, NLTK, OpenAI, langchain, llama-index,kore.ai. DL Architectures: LLM, ANN, CNN, R-CNN, RNN, GRU, LSTM, Transformers, Attention Mechanism, Tokenisers. BERT, T5, Sentence Transformers, Foundational models. Packages More ❯

of Deep learning concepts and architectures like CNNs, RNNs, LSTM, and GANs and ability to apply to real world data sets and problems. Proficiency in ML Modeling - Scikit-Learn, Tensorflow, Keras, Pytorch. Proficiency in NLP Tools SpaCy, ThinC, Gensim. Knowledge of Gen-AI Tools Hugging Face models, OpenAI models, Grok. General competency in various ML disciplines like Classification, Forecasting More ❯

and collaboration skills Full right to work in the UK (we are unable to offer visa sponsorship for this role) Desirable (Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits More ❯

and collaboration skills Full right to work in the UK (we are unable to offer visa sponsorship for this role) Desirable (Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits More ❯

and collaboration skills Full right to work in the UK (we are unable to offer visa sponsorship for this role) Desirable (Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits More ❯

and collaboration skills Full right to work in the UK (we are unable to offer visa sponsorship for this role) Desirable (Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits More ❯

and collaboration skills Full right to work in the UK (we are unable to offer visa sponsorship for this role) Desirable (Not Essential): Familiarity with deep learning frameworks (e.g. TensorFlow, PyTorch) Exposure to cloud platforms (AWS, GCP, or Azure) Experience with experimental design, research methods, or academic publishing Understanding of MLOps, version control (Git), or containerisation (e.g. Docker) Benefits More ❯

hybrid retrieval pipelines Ability to design and operationalize AI agent frameworks and perform prompt/context engineering Software & Infrastructure: Strong programming experience in Python, Java, or C++ Familiarity with TensorFlow, PyTorch, Hugging Face, Scikit-learn, and related ML libraries Experience with Docker, Kubernetes, and cloud AI platforms such as AWS Bedrock, Sagemaker, Azure ML, or GCP Vertex AI Working More ❯

with the application of machine learning and artificial intelligence. Strong programming skills in languages such as Python. Solid understanding of machine learning concepts, algorithms, and libraries (e.g., scikit-learn, TensorFlow, PyTorch). Experience with data manipulation and analysis using tools like Pandas and NumPy. Familiarity with cloud computing platforms (e.g., AWS, Azure, GCP). Preferred Qualifications: Masters degree. Demonstrated More ❯

Effective oral and written communication skills to include active listening Experience with different flavors of Linux including Debian, Red Hat, Ubuntu, etc. Programming in parallel computing Experience with PyTorch, TensorFlow, and Keras Experience working with open-source resources in a government computing environment Pay Transparency: In support of pay transparency at Vantor, we disclose salary ranges on all U.S. More ❯

of experience specifically focused on applied AI/ML 3-5 years of experience with Python and experience building production-grade code Hands-on experience with modern ML frameworks (TensorFlow, PyTorch, scikit-learn, Hugging Face) 2-3 years of experience with at least one major cloud platform (AWS, Azure, or GCP) and their ML services Proven experience applying Generative More ❯

Management, or related field; Master's preferred. • 5-10+ years of experience in AI/ML development and data analytics. • Proficiency in Python and AI/ML frameworks (TensorFlow, PyTorch, scikit-learn, Hugging Face, etc.). • Familiarity with DoD and Federal records management policies, including metadata standards. • Proven experience developing AI solutions in compliance with DoD rules, regulations 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 ❯

science. Proficiency in Python and experience with key NLP libraries and frameworks (e.g., NLTK, spaCy, Transformers, Hugging Face, BERT, Langchain). Hands-on experience with deep learning frameworks like TensorFlow or PyTorch. Strong problem-solving skills, particularly in addressing complex NLP challenges. Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams. We support More ❯

for data science workloads, including data pipelines, model training, and deployment. Experience developing and applying LLMs (e.g. BERT, GPT) for engineering processes and design. Knowledge of deep learning frameworks (TensorFlow, PyTorch, or Keras) and practical experience applying them to real-world problems. Additional programming languages: C++, MATLAB. Experience with MS Power Platform. Web: HTML, browser (e.g., Chrome) based GUI 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 ❯

learning, natural language processing, computer vision, and reinforcement learning. Strong programming skills in Python, R, or other relevant languages. Experience with AI/ML frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, and Keras. Expertise with Large Language Models/Generative AI and Prompt Engineering. Also, using LLM/GAI solutions for usage in human-machine teaming for More ❯

as Matplotlib or Plotly. Machine Learning & Model Development Experience developing and fine-tuning machine learning models, including supervised and unsupervised learning. Applied knowledge of deep learning frameworks such as TensorFlow or PyTorch. Familiarity with transformer-based architectures (e.g., BERT, GPT) and usage of Hugging Face Transformers. Ability to design and implement basic model training pipelines, including data ingestion, preprocessing 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 ❯