101 to 125 of 218 Permanent NumPy Jobs

For: Essential: Experience with LLMs (GPT, BERT) and NLP tasks. Fine-tuning pre-trained models for domain-specific applications. Strong Python skills (OOP, PyTorch, Hugging Face, scikit-learn, Pandas, NumPy). Deploying models with Docker, Kubernetes, or serverless platforms. Familiarity with CI/CD, MLOps, and cloud platforms (AWS preferred). Desirable: Named entity recognition, recommendation systems, or image analysis. More ❯

setting up CI/CD pipelines using tools like Azure DevOps, GitHub Actions, or GitLab CI, targeting Azure environments. Experience with popular Python libraries and frameworks such as Pandas, NumPy, and PySpark. Benefits Full-time, permanent contract. Competitive salary up to £60,000 per annum, depending on experience. Hybrid working model based in Aberdeen (2 days per week in the More ❯

in the UK, and further abroad internationally. Skills And Experience Required Python experience Be great to have a web framework – Flask, FastAPI, Django, etc Data manipulation with Pandas/numpy SQL Server Salary - Up to £56,000 dependent on experience. Hybrid - Flexible 3 days in the office (London SE1 9SG) They are passionate about transforming data into powerful insights that More ❯

with modern data platforms and cloud technologies. Key Responsibilities: Develop, test, and deploy scalable data engineering solutions using Python . Build and maintain data pipelines leveraging libraries such as NumPy, pandas, BeautifulSoup, Selenium, pdfplumber, and Requests . Write and optimize complex SQL queries and manage databases including PostgreSQL . Integrate and automate workflows using DevOps tools (e.g., CI/CD More ❯

with modern data platforms and cloud technologies. Key Responsibilities: Develop, test, and deploy scalable data engineering solutions using Python . Build and maintain data pipelines leveraging libraries such as NumPy, pandas, BeautifulSoup, Selenium, pdfplumber, and Requests . Write and optimize complex SQL queries and manage databases including PostgreSQL . Integrate and automate workflows using DevOps tools (e.g., CI/CD More ❯

teams adopting AI tooling. What You’ll Bring • 5+ years in software/ML engineering, ideally with production deployment experience. • Strong Python background, comfortable across data frameworks like Pandas, NumPy, SciPy, Dask, Polars, or PySpark. • Proven experience setting up ML pipelines, integrating with AWS/Databricks, and applying CI/CD principles. • Solid understanding of time-series forecasting and supervised More ❯

complete the above-mentioned tasks: Experience with machine learning techniques and algorithms, including k-NN, Naive Bayes, SVM, and Decision Forests; Experience with data science toolkits, including R, Weka, NumPy, and MatLab; Experience with data visualization tools, including D3.js and GGplot; Experience using query languages, including SQL, Hive, and Pig; Experience with NoSQL databases, including MongoDB, Cassandra, and HBase; Experience More ❯

into data workflows, unlocking even more potential from their datasets. Skills & Experience Strong Python experience Experience with a web framework – Flask, FastAPI, Django , etc. Solid understanding of Pandas/NumPy for data manipulation SQL Server experience Experience building or maintaining web scraping tools to collect and structure data from online sources Who this role is great for Early-career Python More ❯

implement basic AI algorithms and explore practical applications of AI. Overview of AI and Machine Learning Types of machine learning (supervised, unsupervised, reinforcement) Introduction to Python Libraries for AI - NumPy, Pandas, Matplotlib Scikit-learn for machine learning Building AI Models Data preprocessing Training and evaluating models Advanced Programming for AI Integration This programme aims to equip participants with the knowledge More ❯

concept to production deployment. Skills & Experience Advanced Python skills with a focus on numerical computing and performance. Experience with AI inference acceleration (e.g., TensorRT, ONNX Runtime) and libraries like NumPy, CuPy, SciPy. Expertise in hardware-accelerated video encoding/decoding using Python. Strong communication skills and ability to write maintainable, well-documented code. Self-starter with proven ability to lead More ❯

Exposure to cloud deployments (AWS preferred). Familiarity with MQTT, ZMQ, WebRTC, MIDI, or similar communication protocols. Experience building backend components, APIs, or control-plane systems. Desirable: FFMPEG, PyQt, NumPy, SQLAlchemy experience. Understanding of secure communications (SSL/TLS, JWT). Passion for scalable, highly available architecture (real-time or mission-critical systems). Why Apply? Join a highly technical More ❯

related discipline Demonstrated project experience (academic research, dissertation work, or personal projects) applying machine learning or AI techniques Strong programming skills in Python (e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Familiarity with cloud platforms (AWS, Azure, or GCP) and basic distributed systems concepts Strong problem-solving mindset with a passion for building practical, scalable AI solutions Excellent teamwork and communication More ❯

related discipline Demonstrated project experience (academic research, dissertation work, or personal projects) applying machine learning or AI techniques Strong programming skills in Python (e.g. PyTorch, TensorFlow, scikit-learn, pandas, NumPy) Familiarity with cloud platforms (AWS, Azure, or GCP) and basic distributed systems concepts Strong problem-solving mindset with a passion for building practical, scalable AI solutions Excellent teamwork and communication More ❯

into production-ready features Contributing to both R&D-heavy and commercially driven engineering initiatives What you can bring Strong Python skills, including experience with data processing libraries (e.g. Numpy, Pandas). Bonus: RasterIO or other geospatial tooling experience. Experience with AWS and containerised environments (Docker). Familiarity with backend frameworks (FastAPI, Flask, or Django). Experience working with data More ❯

into production-ready features Contributing to both R&D-heavy and commercially driven engineering initiatives What you can bring Strong Python skills, including experience with data processing libraries (e.g. Numpy, Pandas). Bonus: RasterIO or other geospatial tooling experience. Experience with AWS and containerised environments (Docker). Familiarity with backend frameworks (FastAPI, Flask, or Django). Experience working with data More ❯

experience delivering data migration or integration projects , ideally with ERP/CRM systems such as Microsoft Dynamics . Strong technical skills in Python , including libraries such as Pandas and NumPy . Hands-on experience with ETL design, data warehousing, and relational databases (SQL). Advanced proficiency in SQL and data modelling techniques. Knowledge of cloud data platforms (Azure preferred, AWS More ❯

in MLOps implementation for deploying, monitoring, and managing ML models in production environments. Proficiency in Python and experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy Strong understanding of cloud technologies and AI/ML platforms, particularly AWS SageMaker. Solid grasp of software engineering principles including design patterns, testing, CI/CD, security, and version control More ❯

Data Scientist, with a track record of taking ML projects from conception to deployment in a cloud environment (AWS preferred). Strong proficiency in Python for data science (Pandas, NumPy, Scikit-learn) and SQL (PostgreSQL is a plus). Hands-on experience with the full data lifecycle: data ingestion (e.g., web-scraping with BeautifulSoup, Scrapy, or Selenium), data wrangling, model More ❯

uncertainty quantification, model evaluation, and statistical inference is highly valued. Python expertise: Skilled in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage More ❯

uncertainty quantification, model evaluation, and statistical inference is highly valued. Python expertise: Skilled in building data pipelines and ML models using modern libraries across multiple domains: Data science stack: NumPy, pandas/polars, scikit-learn, XGBoost, LightGBM Deep learning: PyTorch, JAX Statistical programming: NumPyro, PyMC Data skills: Proficient in SQL, with the ability to write efficient, maintainable queries and manage More ❯

Balancing. • CI/CD & DevOps Integration. • AWS (ECS, fargate , EC2, SNS, SQS, Cloudwatch, EKS, Kubernetes). Desirable skills/knowledge/experience: • Familiarity with data analysis libraries like Pandas, NumPy, and Scikit-learn. • Knowledge of data science and machine learning concepts and tools. Good to have: • Strategic thinking and problem-solving. • Collaboration and teamwork. • Effective communication and stakeholder management. • Adaptability More ❯

experience in software development and data engineering with a strong focus on data cleaning, transformation, and creation. Proficiency in Python and experience with data processing libraries (e.g., Pandas, Polars, NumPy). Hands-on experience in building and maintaining automated data pipelines for large-scale data processing. Familiarity with machine learning frameworks (e.g., PyTorch, JAX, scikit-learn) as applied to data More ❯

Data Scientist, Machine Learning Engineer, ML Engineer, Data Science Engineer, High-Performance Engineering, Python, Data, Pandas, numpy, scikit-learn, pytorch - Oxford - up to 55K About the Role: Join an elite engineering organisation's Software Engineering & Data team, where you'll develop cutting-edge machine learning solutions that directly impact performance at the highest level of competition click apply for full More ❯

into technical solutions. Optimize models for scalability, performance, and accuracy. Mentor junior engineers and review code for quality and best practices. Required Skills & Experience Strong proficiency in Python (Pandas, NumPy, Scikit-learn, FastAPI/Flask). Experience with machine learning workflows: data preprocessing, model training, evaluation, and deployment preferably using Microsoft stack - Azure ML, Azure Data Factory, Synapse Analytics, and More ❯

managing cloud-based solutions. Experience with Cloud services such as Azure Data Services, ADLS and AKS. Experience with Python and PySpark for distributed data processing, along with proficiency in Numpy, Pandas and other data manipulation libraries. Experience in optimizing big data architectures for high availability and performance. Strong problem-solving skills, analytical mindset, and ability to work in fast-paced More ❯