and neural networks. Strong programming skills in languages such as Python, R, or Java Familiarity with AI libraries, frameworks, and tools such as TensorFlow, PyTorch, or Keras. Proven understanding of cloud computing platforms (e.g., AWS, Azure, Google Cloud) and experience deploying AI models on these platforms. Solid understanding of AI More ❯
leadership role Extensive experience in building production-grade machine learning systems Strong programming skills in Python, with expertise in ML frameworks such as TensorFlow, PyTorch, and scikit-learn Proven track record of developing predictive models for financial applications Deep understanding of machine learning algorithms, statistical modeling, and data processing techniques More ❯
to computational linguistics are highly desirable, either from a research academic or commercial perspective. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, and Scikit-learn. Experience deploying AI solutions into production environments using AWS/GCP/Azure. Hands-on with MLOps, CI/CD for More ❯
to computational linguistics are highly desirable, either from a research academic or commercial perspective. Proficiency in Python and ML libraries such as Hugging Face, PyTorch, TensorFlow, and Scikit-learn. Experience deploying AI solutions into production environments using AWS/GCP/Azure. Hands-on with MLOps, CI/CD for More ❯
SR2 | Socially Responsible Recruitment | Certified B Corporation™
At least 2 years' experience with machine learning, deep learning, or AI systems. Familiarity with key libraries and frameworks such as TensorFlow, Scikit-learn, PyTorch, Transformers, LangChain, BeautifulSoup, and OpenCV. Experience with AI APIs and platforms (e.g., OpenAI, Bedrock, Gemini, Anthropic). Knowledge of cloud infrastructure (ideally AWS), Kubernetes, and More ❯
a deep understanding of machine learning algorithms, deep learning, and generative models. Advanced AI Skills and Testing: Familiarity with deep learning frameworks (e.g., TensorFlow, PyTorch), hands-on experience in deploying AI/ML solutions as a service/REST API on Cloud or Kubernetes, and proficiency in testing of developed More ❯
Requirements: Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Bioinformatics, or related field. Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch). Experience with bioinformatics tools (e.g., Biopython, RDKit). Strong knowledge of statistical models, deep learning, and data preprocessing. Familiarity with cloud platforms (AWS, GCP More ❯
DevOps, or ML Engineering roles Proven expertise deploying and scaling Generative AI models (GPT, Stable Diffusion, BERT) Proficiency with Python and ML frameworks (TensorFlow, PyTorch, Hugging Face) Strong cloud platform experience (AWS, GCP, Azure) and managed AI/ML services Practical experience with Docker, Kubernetes, and container orchestration Databricks expertise More ❯
as fraud prevention or credit scoring. Machine Learning Expertise: Strong understanding of machine learning algorithms and their practical applications. Experience with frameworks like TensorFlow, PyTorch, and scikit-learn. Data Engineering: Proficiency in developing and maintaining real-time data pipelines. Experience with ETL processes, Python, and SQL. Familiarity with big data More ❯
field (or equivalent experience). Preferred Qualifications: Advanced degree (Master's or Ph.D.) in a relevant field. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch). Knowledge of data visualization tools (e.g., Tableau, Power BI). Familiarity with version control systems (e.g., Git). More ❯
engineering knowledge, including data structures, algorithms, system design, and OOP. You have advanced knowledge of LLM architectures and ML/DL frameworks (e.g. TensorFlow, PyTorch, LangChain, Keras, scikit-learn). You're ready to design, deploy and maintain production-grade Machine Learning systems. You're willing to champion best practices More ❯
to explain complex ideas simply, and work well in cross-functional teams Tech You’ll Work With ML & Data Science Python (primary language) TensorFlow, PyTorch, or Keras NumPy, pandas Data pipelines (Azure Data Factory, Airflow, etc.) Applied ML: NLP, CV, transformers, GANs, time series, etc. Engineering & Cloud Azure (or similar More ❯
multiple release cycles Strong understanding of data science, machine learning concepts, and analytics capabilities Experience with AI/ML frameworks and tools (e.g., TensorFlow, PyTorch, scikit-learn, etc.) Experience with MLOps practices (e.g., model deployment, monitoring, retraining workflows) for operationalizing machine learning models effectively Excellent communication skills with the ability More ❯
science. Strong understanding of AI/ML algorithms and techniques, including LLMs, GenAI, and automated AI systems. Experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and data science libraries (e.g., NumPy, pandas, scikit-learn). Proficiency in Python, R, or other relevant programming languages. Proficiency in working with large datasets More ❯
to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate the capacity of reading, understanding and implementing new techniques in the field of machine learning as they emerge. More ❯
record delivering ML/AI solutions in complex, real-world environments Strong Python skills and experience with key ML libraries (e.g., scikit-learn, XGBoost, PyTorch) Exposure to Generative AI technologies (e.g., LLMs, embeddings, RAG systems) Excellent communication skills and ability to engage senior stakeholders Nice to Have: Experience in consulting More ❯
required: Programming: Proficiency in Python (preferred) and/or other relevant languages (e.g., R, Java, C++). Machine Learning & AI Frameworks: Experience with TensorFlow, PyTorch, Scikit-learn, or similar libraries. Data Manipulation & Analysis: Strong skills in Pandas, NumPy, and SQL for handling and analysing large datasets. Cloud Computing: Familiarity with More ❯
Experience deploying ML models at scale, with knowledge of MLOps, model optimization, and inference acceleration. Proficiency in Python and AI/ML frameworks like PyTorch and TensorFlow. Familiarity with cloud platforms (AWS, GCP, Azure) and containerized environments (Docker, Kubernetes). Ability to communicate complex AI concepts clearly to both technical More ❯
coding background (e.g., ACM/ICPC, NOI/IOI, Top Coder, Kaggle). Technical Skills : Expertise in machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch), Generative AI technologies, and libraries like Langchain, Weaviate, Langgraph, LlamaIndex. Track Record : Demonstrated success in applying data science and machine learning to solve real-world More ❯
AI/ML software. Strong expertise in machine learning and neural network algorithms. Proficient in programming languages like Python, and libraries such as TensorFlow, PyTorch, NumPy and LangChain Experience with server deployment, cloud computing environments, and API development. Good understanding of software engineering best practices. Excellent problem-solving abilities and More ❯
of experience in machine learning engineering, with a strong focus on productionizing ML models and MLOps. Proficiency in Python and ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn). Strong experience with cloud platforms (AWS, GCP, or Azure) and containerization (Docker, Kubernetes). Hands-on experience with CI/CD pipelines More ❯
to-end solutions Implement testing, monitoring, and performance optimisation of ML systems Contribute to architectural discussions and promote engineering best practices Technical Environment: Python, PyTorch or TensorFlow AWS (including SageMaker, S3, Lambda) or Azure ML Docker, Kubernetes, Airflow CI/CD tools (e.g. GitHub Actions, Jenkins) MLflow or similar frameworks More ❯
learning and popular augmentation techniques Able to apply machine learning to real-life problems Vision Transformers, DeepLabv3, SegFormer, etc.Python, Scikit-Learn, NumPy, Pandas and PyTorch/TensorFlow/Keras Thrive in a cross-functional working environment Possess a clear passion for data science beyond the day job Nice-to-haves More ❯
with a self-starter mindset. Proven track record with NLP applications and transformer-based models like BERT or GPT. Advanced proficiency in Python, including PyTorch, Hugging Face Transformers, Pandas, and scikit-learn. Strong understanding of cloud services (preferably AWS) and experience with Docker, Kubernetes, MLFlow, Kubeflow, or similar MLOps tools. More ❯
in machine learning, deep learning, or AI techniques (e.g., supervised learning, neural networks, NLP). Proficiency in Python and libraries like Scikit-learn, TensorFlow, PyTorch, or Hugging Face Transformers. Familiarity with data preprocessing, model evaluation, and deploying models in real-world environments. A passion for AI innovation and a desire More ❯
