Staff AI Engineer
Delhi NCRFull-TimeStaffAI / Data Science
You will:
- Design and develop an ensemble of classical and deep learning algorithms for modeling complex interactions between people, software, infrastructure and policies in an enterprise environment
- Design and implement algorithms for statistical modeling of enterprise cybersecurity risk
- Apply data-mining, AI and graph analysis techniques to address a variety of problems including modeling, relevance and recommendation.
- Build production quality solutions that balance complexity and performance
- Participate in the engineering life-cycle at Balbix, including designing high quality ML infrastructure and data pipelines, writing production code, conducting code reviews and working alongside our infrastructure and reliability teams
- Drive the architecture and the usage of open source software library for numerical computation such as TensorFlow, PyTorch, and ScikitLearn
You are:
- Able to take on very complex problems, learn quickly, iterate, and persevere towards a robust solution
- Product-focused and passionate about building truly usable systems
- Collaborative and comfortable working across teams including data engineering, front end, product management, and DevOps
- Responsible and like to take ownership of challenging problems
- A good communicator, and facilitate teamwork via good documentation practices
- Comfortable with ambiguity and thrive in designing algorithms for evolving needs
- Intuitive in using the right type of models to address different product needs
- Curious about the world and your profession, constant learner
You have:
- A Ph.D./M.S. in Computer Science or Electrical Engineering with hands-on software engineering experience
- 5+ years of experience in the field of Machine Learning and programming in Python.
- Expertise in programming concepts and building large scale systems.
- Knowledge of state-of-the-art algorithms combined with expertise in statistical analysis and modeling.
- Robust understanding of NLP, Probabilistic Graphical Models, Deep Learning with graphs structures, model explainability, etc.
- Foundational knowledge of probability, statistics and linear algebra
