AI / ML Engineer

Responsibilities:
In this role, you are required to do analysis and solve moderately complex problems.
Typically creates new solutions, leveraging and, where needed, adapting existing methods and procedures.
The person requires understanding of the strategic direction set by senior management as it relates to team goals.
Primary upward interaction is with direct supervisors or team leads.
Generally interacts with peers and/or management levels at a client and/or within Accenture.
The person should require minimal guidance when determining methods and procedures on new assignments.
Decisions often impact the team in which they reside and occasionally impact other teams.
The individual would manage medium- to small-sized teams and/or work efforts (if in an individual contributor role) at a client or within Accenture.
Please note that this role may require you to work in rotational shifts.
Requirements:
Experience in machine learning, machine learning algorithms, Microsoft Azure Machine Learning, Python (programming language), Python software development, ability to work well in a team, written and verbal communication, numerical ability, and results orientation.
CL8 AI Research Scientist: Deep Expertise in Machine Learning and AI Theory.
Algorithm Design and Theoretical Innovation: Data Proficiency and Synthetic Data Generation, Responsible AI and Ethical Awareness.
Programming and Tooling: Proficiency in Python, TensorFlow, PyTorch, and JAX; cloud-based AI platforms (e. g., Azure OpenAI, AWS SageMaker); version control, containerization (Docker), and MLOps pipelines. The Azure OpenAI Service L100 deck also emphasizes the evolution of tooling from symbolic AI to modern generative models
Collaboration and Communication: AI research scientists often work in interdisciplinary teams and must publish research presented at conferences and collaborate with engineers, product teams, and stakeholders.
ML Research Engineers: Mathematical and Statistical Foundations, Programming and Software Engineering, Model Development and Experimentation
MLOps and Deployment: Data Engineering and Feature Engineering.

Experience
8-12 yrs