We are looking for a passionate and talented mid to senior level ML/AI engineer, with a strong background in software development and applied AI, to help build our industry-leading peer-group identification technology. Our mission is to push the envelope in company analysis technology to provide our users with the best possible analytical experience.
As an ML/AI Engineer, you will work with a world-class team to assist the R&D of ML/AI technology. You will leverage our heterogeneous data sources and large-scale computing resources to develop the MLOps infrastructure, while facilitating scientists in developing novel machine learning models.
This role requires pragmatic technical leadership, comfort with ambiguity and capabilities to simplify complex MLOps pipelines through clear visual and written explanations.
The ideal candidate will have experience creating efficient MLOps infrastructures for building AI-based products. We are particularly interested in experience with training and deploying natural language processing, graph learning, deep learning, and reinforcement learning models at scale. Additionally, we seek candidates with strong rigor in software development, creativity, curiosity, and excellent judgment.
What you will be doing
- Build research and development pipelines for machine learning, deep learning, NLP, NLU, graph learning or reinforcement learning/user intent modelling systems
- Collaborate closely with in-house scientists on Machine Learning Operations MLOps and tasks ranging from data management to training and deployment of models
- Develop data collections, training and evaluation pipelines for research and development of machine learning models
- Support deployment of models in collaboration with software engineers to integrate successful research results into the production systems
- Initiate and define new MLOps ideas to productize the peer-group analysis technology efficiently and drive its execution
- Work with the tech stack of Python/Tensorflow/Keras/Pytorch/Jupyter notebooks and cloud computing and storage platforms
- Write reusable, testable, and efficient code
- Work closely with our developers/engineers to supply them with vital tools
What we are looking for
- Profound expertise in MLOps (collect, store, manage data and models), creating training datasets (data labelling, feature engineering, data partitioning, sampling and slicing), building and training machine learning infrastructure, model deployment in production (inference constraints, model compression, evaluations)
- Knowledge of ML infrastructure monitoring and maintenance
- Familiarity with architectural choices for ML systems
- Prior experience working in collaboration with Machine Learning scientists
- Master or PhD in Computer Science or equivalent industrial experience working with Machine Learning models in information retrieval, search, information discovery or recommender systems
- Hands-on experience implementing machine learning systems at scale in Python or similar languages and a variety of libraries like Tensorflow/Keras/Pytorch/Numpy/Pandas/Scikit-learn
- Understanding of fundamental design principles behind a scalable AI and data-driven applications
- Solid experience of working with well-known cloud computing and web services such as AWS
- Good coding skills and engineering practices with agile software process and research-driven development
- Knowledge of DevOps methodologies and project management tools such as Git, Jira, Confluence..,
- Good written and spoken English communication skills and be able to present complex solutions with clarity
- Flexible and responsive to spontaneous needs, challenges and opportunities, able to balance conflicting demands on time and priorities
- Highly self-organised, planned and collaborative with a strong personal drive to keep up with a productive mindset
- Continual passion to learn and apply knowledge in a methodical fashion
You might also have
- Deeper knowledge in fields such as Statistical modeling, Deep Learning, NLP, NLU, Graph learning or Reinforcement Learning/User intent modelling
- Familiarity with Computer Science fundamentals in algorithm design, complexity analysis, data structures, problem-solving and diagnosis
How to apply
If you are interested in this role and you would like to learn more, please contact Muhammad Ammad-ud-din at email@example.com or send your resume/CV directly to