101 to 125 of 341 Permanent TensorFlow Jobs

and deployment of ML models into production environments. Expertisein Python and relevant ML libraries Experience with various ML algorithms (e.g. supervised, unsupervised, reinforcement learning) and deep learning frameworks (e.g. TensorFlow,PyTorch). Experience developing generative AI applications and deploying them into production. Experience working with cloud platforms, ideally Google Cloud Platform (GCP) andBigQuery. Proficiencyin working with and querying global More ❯

About You: Experience: 5+ years of experience in machine learning engineering, preferably in Ad Tech, Mar Tech, or related high-scale environments. Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Deep understanding of supervised, unsupervised, and reinforcement learning methods. Experience with GCP services such as Vertex AI, BigQuery ML, Dataflow, AI Platform Pipelines, and Dataproc. More ❯

AI landscape, identifying and adopting new techniques, tools, and methodologies as appropriate. Requirements: Excellent programming ability in Python and good experience with machine learning libraries such as PyTorch (preferred), TensorFlow, OpenCV etc. Experience in deploying, maintaining, and optimising deep learning pipelines, focusing on efficiency, performance, and production maturity. Strong understanding of machine learning principles, deep learning techniques and GenAI More ❯

About You: Experience: 5+ years of experience in machine learning engineering, preferably in Ad Tech, Mar Tech, or related high-scale environments. Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Deep understanding of supervised, unsupervised, and reinforcement learning methods. Experience with GCP services such as Vertex AI, BigQuery ML, Dataflow, AI Platform Pipelines, and Dataproc. More ❯

About You: Experience: 5+ years of experience in machine learning engineering, preferably in Ad Tech, Mar Tech, or related high-scale environments. Strong proficiency in Python and ML frameworks (TensorFlow, PyTorch, Scikit-learn). Deep understanding of supervised, unsupervised, and reinforcement learning methods. Experience with GCP services such as Vertex AI, BigQuery ML, Dataflow, AI Platform Pipelines, and Dataproc. More ❯

with cloud AI services, model deployment, monitoring, and CI/CD pipelines for ML models (MLOps best practices). Example Tools & Technologies: Frameworks & Libraries: LangChain, Hugging Face Transformers, PyTorch, TensorFlow, Scikit-learn Agentic AI Tools: OpenAI GPT models, Crew,AI, Cohere, Pinecone (for vector databases), AutoGPT Data Engineering & ML Pipelines: Apache Airflow, MLflow, Kubeflow, dbt, Prefect Cloud & Deployment Platforms More ❯

names in private equity. Ideal Candidate Experience: 5+ years in data science roles, preferably in fast-moving or early-stage environments Languages & Tools: Strong Python (Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch) Advanced SQL AWS MLOps tooling (e.g., MLflow, SageMaker, or similar) Bonus Points For: Knowledge of LLMs, RAG pipelines, prompt engineering, or agentic interfaces Experience with data warehouse More ❯

names in private equity. Ideal Candidate Experience: 5+ years in data science roles, preferably in fast-moving or early-stage environments Languages & Tools: Strong Python (Pandas, NumPy, Scikit-learn, TensorFlow or PyTorch) Advanced SQL AWS MLOps tooling (e.g., MLflow, SageMaker, or similar) Bonus Points For: Knowledge of LLMs, RAG pipelines, prompt engineering, or agentic interfaces Experience with data warehouse More ❯

orchestration and a strong grasp of DevSecOps best practices in cloud-native environments • Hands-on experience with key tools and frameworks, including: o Python, NumPy, Pandas, scikit-learn o TensorFlow or PyTorch o Elasticsearch, Logstash, Kibana • Preferred Experience in GDS (Government Digital Services) or USDS (U.S. Digital Services) Education Requirement • Shall have at a minimum, a bachelor's degree More ❯

of concepts of new technologies and evaluating the state of the art in machine learning algorithms for text analytics Demonstrated experience with machine learning frameworks such as PyTorch, Keras, Tensorflow Demonstrated experience with data visualization tools (i.e. Tableau, Pandas, D3.js, ggplot, etc) Demonstrated experience with NoSQL data stores such as MongoDB or DynamoDB Demonstrated experience using Natural Language Processing More ❯

equivalents (e.g., logging, tracing, metrics). Knowledge of data processing frameworks (e.g., Pandas, Spark, Airflow) is a plus. Comfortable reading and working with Python-based ML code (scikit-learn, TensorFlow, PyTorch, etc.). Strong ownership mindset and a collaborative attitude. Nice to Have Experience with model versioning and ML serving frameworks (e.g., MLflow, Seldon, Triton). Understanding of data More ❯

years, or PhD with 4 years Deep expertise in big data platforms (e.g., Hadoop, Spark, Kafka) and multi-cloud environments (AWS, Azure, GCP) Experience with machine learning frameworks (e.g., TensorFlow, Scikit-learn, PyTorch) Strong programming skills in Python, Java, or Scala Familiarity with data pipeline development and real-time data processing Proven ability to design and implement data-intensive More ❯

we need from you: Strong background in NLP and AI, particularly with LLMs and RAG-based solutions Proficiency in Python and modern AI/ML libraries (e.g. HuggingFace, LangChain, TensorFlow, PyTorch) Experience with data exchange and storage frameworks (e.g. APIs, SQL, NoSQL, Parquet) Track record of delivering technical solutions in Agile environments Excellent communication skills and a collaborative mindset More ❯

Data Science and across Jazz Research and Development. Required Knowledge, Skills and Abilities Strong programming skills in Python, R, or similar languages, with experience in modern ML frameworks (PyTorch, TensorFlow). Demonstrated experience with generative AI technologies, including LLM architectures and frameworks. Experience with natural language processing and generative AI for medical text analysis, generation, and interpretation. Demonstrated ability More ❯

Strong engineering fundamentals, including DevOps, CI/CD, and secure ML/AI pipelines (DevSecOps). Highly proficient in Python, SQL, and key AI/ML frameworks (e.g., PyTorch, TensorFlow). Ability to mitigate model hallucination, optimise performance, and lead governance around AI risk and ethical deployment. Ready to Transform the Future? This a career-defining opportunity working within More ❯

Strong engineering fundamentals, including DevOps, CI/CD, and secure ML/AI pipelines (DevSecOps). Highly proficient in Python, SQL, and key AI/ML frameworks (e.g., PyTorch, TensorFlow). Ability to mitigate model hallucination, optimise performance, and lead governance around AI risk and ethical deployment. Ready to Transform the Future? This a career-defining opportunity working within More ❯

What You'll Bring: Degree in Computer Science, AI, ML, or a related field. Experience in developing and deploying AI/ML solutions. Proficiency in Python and ML frameworks (TensorFlow, PyTorch). Strong understanding of LLMs, NLP, and machine learning algorithms. MLOps knowledge and experience with version control systems. Back-end engineering skills in Python or Node.js (willingness to More ❯

related field • Proven experience in data science and machine learning, with a focus on natural language processing and LLMs. • Proficiency in programming languages such as Python and libraries like TensorFlow, PyTorch, or similar. • Strong understanding of data preprocessing techniques, feature engineering, and model evaluation metrics. • Experience with secure data handling and the ability to work within restricted environments. • Excellent More ❯

Experience with neural deep learning methods and machine learning - Experience with prompting techniques for LLMs PREFERRED QUALIFICATIONS - Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc. - Experience with large scale distributed systems such as Hadoop, Spark etc. - PhD in math/statistics/engineering or other equivalent quantitative discipline - Experience with conducting More ❯

commerce - this is essential as you'll be the domain expert from day one. Excellent Python programming skills and strong familiarity with ML libraries such as scikit-learn, XGBoost, TensorFlow, PyTorch, Keras etc. Proven track record deploying ML models to production (API, batch, or streaming contexts) Solid understanding of the modern software engineering, infrastructure and data technologies Experience working More ❯

the ability to collaborate effectively across departments Strong project management and organisational skills Proficiency in programming languages such as Python, Java, or Scala, and experience with ML frameworks like TensorFlow, PyTorch, or scikit-learn Experience with large-scale distributed systems and big data technologies (e.g., Spark, Hadoop, Kafka) Accommodation requests If you need assistance with any part of the More ❯

ML workflow orchestration tools (e.g., Kubeflow, Ray, Airflow, Metaflow). Excellent programming skills in Python; experience with Bash, Go, or C++ is beneficial. Strong understanding of ML frameworks (PyTorch, TensorFlow, JAX) and familiarity with distributed training methods, GPU acceleration, and optimization libraries (e.g., XLA, NCCL). Excellent understanding of software development best practices, CI/CD, and automation. Familiarity More ❯

and scaling models that solve complex, real-world problems Deep understanding of machine learning concepts and experience applying them in production settings, using frameworks such as Transformers, PyTorch, or TensorFlow Strong Python skills, with the ability to write clean, modular, production-grade code, and a solid understanding of data engineering and MLOps principles Ability to lead end-to-end More ❯

and generative AI. Able to contextualise these techniques in industry settings, such as financial forecasting, operations optimisation, or customer segmentation. Comfortable with key ML frameworks (such as Scikit-learn, TensorFlow, PyTorch, Hugging Face) and data manipulation tools (Pandas, NumPy), as well as version control, containerisation, and ML deployment pipelines. Understands how to apply MLOps principles in production environments To … in machine learning and/or data science roles Solid understanding of MLOps and production deployment practices Experience with Python and core ML libraries (e.g., Scikit-learn, Pandas, PyTorch, TensorFlow) Familiarity with cloud platforms and data infrastructure (e.g., AWS/GCP/Azure, SQL, ELT tools) Understanding of ethical frameworks, explainability, and governance in AI Engaging & Adaptable Communication: Ability More ❯

well as similarity metrics. Excellent interpersonal skills and a strong collaborator. Solid applied statistics skills, such as descriptive statistics, distributions analysis, and statistical testing. Proficient in Python: GenAI Magic: TensorFlow, PyTorch, Hugging Face Transformers, Sentence Transformers, OpenAI API. More ❯