Associate Principal Engineer (Machine Learning)

REQUIREMENTS:

• Strong expertise in Python and data science on Azure.
• Extensive experience in machine learning solution design, including NLP, machine vision, and AI.
• Proficiency in deep learning frameworks such as TensorFlow, PyTorch, or JAX, and machine learning libraries such as scikit-learn.
• Practical experience with Generative AI frameworks such as GANs, VAEs, prompt engineering, and retrieval-augmented generation (RAG).
• Strong understanding of MLOps principles and tools, with experience in CI/CD pipelines, model monitoring, and automated deployment.
• Excellent communication and collaboration skills.
• Proven leadership and mentorship abilities.
• Strong client interaction and technical consulting skills.

RESPONSIBILITIES:

• Understanding the clients business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.
• Mapping decisions with requirements and be able to translate the same to developers.
• Identifying different solutions and being able to narrow down the best option that meets the client’s requirements.
• Defining guidelines and benchmarks for NFR considerations during project implementation
• Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers
• Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed.
• Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies, patterns, and frameworks to materialize it
• Understanding and relating technology integration scenarios and applying these learnings in projects
• Resolving issues that are raised during code/review, through exhaustive systematic analysis of the root cause, and being able to justify the decision taken.
• Carrying out POCs to make sure that suggested design/technologies meet the requirements.