Come join a team of industry and science leaders to achieve a vision of empowering innovation through state of the art artificial intelligence and machine learning. We are addressing exciting challenges for our customers, at the intersection of AI/ML and cutting-edge cloud infrastructure, with NLP being a core area of what we do and what we offer our customers.
What you would do
- Write production code.
- Design, architect, and implement robust infrastructure solutions for AI systems, ensuring optimal performance, scalability, and cost-efficiency while considering the unique requirements and complexities associated with artificial intelligence.
- Champion the adoption of cutting-edge data science methodologies within the team, fostering an environment that prioritizes system scalability and seamless development processes.
- Transform AI/ML prototypes developed by our scientists into industry-grade products, facilitating the seamless integration of aiXplain’s platform for both existing and prospective clients.
- Drive innovation and excellence in software engineering by designing, developing, testing, deploying, documenting, maintaining, and enhancing software solutions, while effectively managing project priorities, deadlines, and deliverables to ensure the timely delivery of high-quality products.
- BSc in Computer/Software Engineering, Science, or a related technical field.
- Minimum of 2 years in software development.
- Experience writing production code.
- Ability to handle ambiguity and thrive in a highly collaborative tech-startup environment, while maintaining a customer-centric approach.
- Advanced proficiency in Python programming language.
- Familiarity with unit testing tools, such as Pytest.
- Proficient in using Github for code management.
- Strong experience with multithreading and multiprocessing programming (e.g., Celery, Ray).
- Good experience in deploying models using FastAPI.
- Well-versed with SQL/NoSQL databases and Big-Data platforms (e.g., MongoDB, Hadoop, Elastic-search, Redis).
Nice to haves
- Experience in designing, building, and deploying end-to-end ML pipelines using deep learning frameworks such as Scikit-learn, PyTorch, and TensorFlow.
- Familiarity with MLOps and AutoML platforms, including Kubeflow, MLflow, Kubernetes, and Docker.
- Experience with cloud infrastructure orchestration using scripting tools.