1 to 25 of 760 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 ❯

. Skilled in integrating ML models with databases (SQL, NoSQL) and delivering seamless user experiences through robust software engineering. Proficient in Python (pandas, scikit-learn), deep learning (PyTorch/TensorFlow/Keras), NLP/LLMs, LangChain, embeddings/vector search, and classic ML. Experienced with Snowflake-native ML (Snowpark Python, UDFs/UDTFs, Tasks/Streams). Competent in 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 ❯

novel solutions. Stay ahead of AI research, especially in Generative AI, applying the latest findings and techniques to drive innovation within our projects. Proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn). Strong background in cloud computing and big data platforms (AWS, Azure, GCP), with hands-on experience in cloud-based ML services and serverless architectures. Familiarity More ❯

novel solutions. Stay ahead of AI research, especially in Generative AI, applying the latest findings and techniques to drive innovation within our projects. Proficiency in Python and ML libraries (TensorFlow, PyTorch, scikit-learn). Strong background in cloud computing and big data platforms (AWS, Azure, GCP), with hands-on experience in cloud-based ML services and serverless architectures. Familiarity More ❯

DS best practices, give technical recommendations on challenging problems. Qualifications Core Programming & Machine Learning: Proficiency in Python and deep experience with its data science and ML ecosystem (e.g., PyTorch, TensorFlow, scikit-learn, Pandas, NumPy). Hands-on experience with Generative AI frameworks and libraries such as LangChain, LlamaIndex, or Hugging Face Transformers. MLOps & Infrastructure: Expertise in building and maintaining More ❯

strategic challenges. Proficiency in designing, implementing, and maintaining MLOps processes in a cloud environment (e.g., Azure, AWS, GCP). Technical Skills: Expertise in Python and its ML ecosystem (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy). Strong background in statistical analysis, algorithm design, and software engineering best practices. Experience with Docker and Kubernetes for containerization and orchestration. Proficiency with More ❯

strategic challenges. Proficiency in designing, implementing, and maintaining MLOps processes in a cloud environment (e.g., Azure, AWS, GCP). Technical Skills: Expertise in Python and its ML ecosystem (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy). Strong background in statistical analysis, algorithm design, and software engineering best practices. Experience with Docker and Kubernetes for containerization and orchestration. Proficiency with More ❯

learning concepts, algorithms, and techniques Expert in software engineering principles and software development methodologies Proficiency in Python Experience with latest MLOps tools and machine learning frameworks/libraries (e.g., TensorFlow, PyTorch, scikit-learn) Demonstrated ability to preprocess, analyze, and visualize large datasets Experience with cloud platforms (e.g. AWS, GCP, Azure), related compute and storage systems, and with deploying machine 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 ❯

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 ❯

is a plus. Experience: Proven experience (2+ years) working with machine learning, deep learning, or AI technologies. Familiarity with a wide range of machine learning frameworks and libraries (e.g., TensorFlow, PyTorch, Keras, Scikit-learn, etc.). Experience with cloud platforms (AWS, Azure, Google Cloud) for deploying AI models is preferred. Skills: Strong proficiency in programming languages such as Python More ❯

learning, or a related field, with a track record of delivering impactful data solutions. Proficiency in Python and experience with data science libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, or PyTorch. Strong experience with SQL and working knowledge of data warehouse systems such as Amazon Redshift, Google BigQuery, or Snowflake. Expertise in statistical analysis, machine learning algorithms, and 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 ❯

Experience working with Python, SQL and noSQL databases Experience with one or more data science and machine/deep learning frameworks and tooling, including scikit-learn, H2O, keras, pytorch, tensorflow, pandas, numpy, carot, tidyverse Command of data science and statistics principles (regression, Bayes, time series, clustering, P/R, AUROC, exploratory data analysis etc ) Strong knowledge of key programming More ❯

including optimization for AI workloads Experience building and optimizing retrieval-augmented generation (RAG) systems Experience architecting and implementing multi-agent AI systems Knowledge of deep learning frameworks (PyTorch or TensorFlow) and NLP, particularly transformer architectures Experience with cloud platforms (AWS preferred) and containerization (Docker, Kubernetes) Experience building solutions using pre-trained LLMs (OpenAI, Claude, Llama, etc.) Strong background in More ❯

including optimization for AI workloads Experience building and optimizing retrieval-augmented generation (RAG) systems Experience architecting and implementing multi-agent AI systems Knowledge of deep learning frameworks (PyTorch or TensorFlow) and NLP, particularly transformer architectures Experience with cloud platforms (AWS preferred) and containerization (Docker, Kubernetes) Experience building solutions using pre-trained LLMs (OpenAI, Claude, Llama, etc.) Strong background in More ❯

Python, C++, or Java with an understanding of algorithms and data structures. Strong expertise in Python data science stack (NumPy, Pandas) and ML/DL frameworks (scikit-learn, PyTorch, TensorFlow) for end-to-end model development. 3+ years building production-ready ML infrastructure, including data pipelines, training/inference workflows, and deployment automation. Solid understanding of software engineering best 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 ❯

Apache Flink, or similar streaming frameworks Strong background in database technologies, including both SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra, Redis) systems Hands-on experience with machine learning frameworks (TensorFlow, PyTorch, scikit-learn) and MLOps practices for production ML systems Expertise in observability and monitoring tools and practices, with experience integrating with platforms like Prometheus, Grafana, or ELK stack More ❯

additional years of experience in lieu of degree) Proficiency with open-source programming languages (e.g. Python, R, SQL) and machine learning toolkits (e.g. pytorch, numpy, polars, scikit-learn, tensorflow, pandas) Proficiency using mathematical, statistical, or other data-driven analysis Proficiency with APIs; both development/update of internal APIs and use of external APIs Experience with data manipulation, analysis More ❯

Deep expertise in database technologies, including both SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Cassandra, Redis) systems with experience in data modeling and optimization Advanced experience with machine learning frameworks (TensorFlow, PyTorch, scikit-learn) and MLOps practices for production ML systems at scale Expert knowledge of observability and monitoring tools and practices, with experience architecting solutions using Prometheus, Grafana, ELK More ❯

or a related field. 5+ years of experience in designing and deploying machine learning models in production. Strong programming skills in Python and proficiency in ML frameworks such as TensorFlow, PyTorch, or Scikit-Learn . Experience with cloud platforms (AWS preferred) and containerization technologies (Docker, Kubernetes). Proficiency in distributed computing frameworks like Spark, Ray, or Dask . Experience More ❯

Experience with data visualization tools (Tableau, Power BI, Matplotlib) Experience with natural language processing (NLP) and text analytics Proficiency in Python, R, SQL, and machine learning frameworks (Scikit-learn, TensorFlow, PyTorch). Familiarity with data security protocols for classified environments Preferred Qualifications: Experience supporting Joint Intelligence or U.S. Geographic Combatant CommandsExperience with advanced analytic tools (Open.IO, Echosec, Babel Street More ❯