as well as cloud-based data storage solutions (e.g., AWS S3, Google Cloud Storage). Experience with AI/ML tools and frameworks (e.g., TensorFlow, PyTorch) and integrating data pipelines with these tools. Familiarity with containerization technologies (e.g., Docker, Kubernetes) and cloud platforms (e.g., AWS, GCP, Azure). Strong More ❯
libraries such as NLTK, spaCy, or Hugging Face Transformers. Familiarity with time-series databases and analysis tools. Knowledge of AI model serving frameworks like TensorFlow Serving or ONNX Runtime. Experience with AI ethics and bias mitigation techniques. Familiarity with GPU acceleration and distributed computing for AI workloads. Why Join More ❯
successful match you must have: 1+ years in a Machine Learning or ML Engineering role, with hands-on experience in deep learning frameworks (e.g., TensorFlow, PyTorch). Motivated recent graduates are encouraged to apply! A degree in Mathematics, Engineering, Statistics, Computer Science, Physics, or a related field. An advanced More ❯
in machine learning/NLP roles, with recent focus on LLMs and/or GenAI. Strong proficiency in Python, deep learning frameworks (PyTorch or TensorFlow), and GenAI libraries (LangChain, LlamaIndex, Transformers). Hands-on experience with vector search, embedding models, and retrieval pipelines. Familiarity with prompt engineering, prompt tuning More ❯
in machine learning/NLP roles, with recent focus on LLMs and/or GenAI. Strong proficiency in Python, deep learning frameworks (PyTorch or TensorFlow), and GenAI libraries (LangChain, LlamaIndex, Transformers). Hands-on experience with vector search, embedding models, and retrieval pipelines. Familiarity with prompt engineering, prompt tuning More ❯
models. - Experience communicating across technical and non-technical audiences. - Experience in using Python and hands-on experience building models with deep learning frameworks like TensorFlow, Keras, PyTorch, MXNet. - Fluency in written and spoken English. PREFERRED QUALIFICATIONS - Knowledge of AWS tech stack (e.g., AWS Redshift, S3, EC2, Glue). - PhD More ❯
ICML, EMNLP, CVPR, etc.) and/or journals Strong high-level programming skills (e.g., Python), frameworks and tools such as DeepSpeed, Pytorch lightning, kubeflow, TensorFlow, etc. Strong written and verbal communication skills to operate in a cross functional team environment More ❯
frameworks like Streamlit) for dashboard creation. Experience with Machine Learning model development and data science workflows (including frameworks such as scikit-learn, PyTorch, or TensorFlow). Experience in Quantitative Finance or strong interest in mathematical/financial modeling, derivatives pricing, or algorithmic trading. Familiarity with ETRM platforms (e.g., OpenLink More ❯
frameworks like Streamlit) for dashboard creation. Experience with Machine Learning model development and data science workflows (including frameworks such as scikit-learn, PyTorch, or TensorFlow). Experience in Quantitative Finance or strong interest in mathematical/financial modeling, derivatives pricing, or algorithmic trading. Familiarity with ETRM platforms (e.g., OpenLink More ❯
data querying and manipulation. Experience with Git, Bash, Docker, and machine learning pipelines. Experience with open-source machine learning libraries like scikit-learn, PyTorch, TensorFlow, and SciPy. Hands-on experience working with multi-modal data (images, text) and relevant ML techniques. Experience with cloud technologies and data storage solutions More ❯
practical experience). 2-3 years of experience in machine learning, with a strong understanding of core ML algorithms and frameworks (e.g., scikit-learn, TensorFlow, PyTorch). Hands-on experience with data preprocessing, feature engineering, and model training for real-world problems. Strong Python and Java programming skills and More ❯
strengths and interests, you may be asked to work outside your job description. Our tech stack Languages: Python (PyTorch, NumPy, OpenCV) ML Frameworks: PyTorch, TensorFlow, OpenCV Infrastructure: AWS, GCP, Docker, Kubernetes, MLFlow MLOps Tools: DVC, Weights & Biases, TensorRT Version Control & CI/CD: GitHub, GitLab, Jenkins Key Responsibilities Develop More ❯
timeframe An excellent command of the basic libraries for data science (e.g. NumPy, Pandas, Scikit-Learn) and familiarity with a deep-learning framework (e.g. TensorFlow, PyTorch, Caffe) A high level of mathematical competence and proficiency in statistics A solid grasp of essentially all of the standard data science techniques More ❯
ideal candidate has a strong background in software development best practices, mathematics, and statistics, as well as experience with machine learning frameworks such as TensorFlow or PyTorch. Candidates should also have excellent programming skills in Python and be familiar with data processing and analysis tools. Candidates should be able More ❯
in top-tier conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL, ICLR). Familiarity with one or more deep learning frameworks (e.g. pytorch, jax, tensorflow, ) Experience in ML Research beyond completing a PhD (e.g. supervision, industry experience, leading academic initiatives, ). Excellent communication skills to report and present research More ❯
Experience working with ML models and their deployment. Familiarity with ML infrastructure, feature stores, and MLOps best practices. Exposure to deep learning frameworks (PyTorch, TensorFlow). Experience with building internal tools, dashboards, or lightweight front-end components (e.g., Streamlit, Dash, or BI tools like Looker, Tableau) or JavaScript frameworks More ❯
industry experience, or an MS with significant industry or research experience in the field. Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas). Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model More ❯
understanding of industry trends, concepts, opportunities, threats & constraints. Experience & knowledge of GenAI, predictive analytics, or automation workflows in enterprise environments. Familiarity with NLP frameworks (TensorFlow, PyTorch) and LLM fine-tuning techniques. ITIL certification and/or AI/ML certifications (AWS, Google Cloud). Experience with implementing scripted web More ❯
london, south east england, United Kingdom Hybrid / WFH Options
Tata Consultancy Services
understanding of industry trends, concepts, opportunities, threats & constraints. Experience & knowledge of GenAI, predictive analytics, or automation workflows in enterprise environments. Familiarity with NLP frameworks (TensorFlow, PyTorch) and LLM fine-tuning techniques. ITIL certification and/or AI/ML certifications (AWS, Google Cloud). Experience with implementing scripted web More ❯
years 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/ More ❯
years 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/ More ❯
for building language model applications. Proficiency in Python and SQL, with strong skills in data manipulation and analysis. Expertise in AI frameworks such as TensorFlow, PyTorch, and Hugging Face Transformers. Ability to effectively communicate complex AI concepts, especially to non-technical stakeholders. Preferred Qualifications Experience with graph databases and More ❯
coursework 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 More ❯
coursework 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 More ❯
Implementing MLOps best practices for seamless model deployment, monitoring, and iteration. What We’re Looking For: 🔹 Strong Python skills – You know your way around TensorFlow, PyTorch, or Hugging Face. 🔹 Experience with LLMs, NLP, or recommendation algorithms . 🔹 Familiarity with MLOps, APIs, and scalable cloud solutions . 🔹 Passion for AI More ❯
