Project Role : AI / ML Engineer
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : Data Science
Good to have skills : NA
Minimum 15 year(s) of experience is required
Educational Qualification : 15 years full time education
Roles & Responsibilities:
- Define and drive ML strategy across identified use cases, ensuring alignment with delivery objectives.
- Lead a team of data scientists and ML engineers, providing technical direction and mentorship.
- Establish feature engineering direction and standards across the data science workstream.
- Design and oversee experiment tracking frameworks and model validation strategies.
- Own end-to-end ML model lifecycle — from evaluation and selection through to production deployment.
- Evaluate and select appropriate ML models, including gradient boosting and ensemble approaches.
- Collaborate with data engineering and architecture teams to ensure model-ready data pipelines.
- Provide hands-on guidance on Databricks, including use of Databricks-native models and MLflow.
Professional & Technical Skills:
- ML Strategy: Proven ability to define ML strategy across multiple concurrent use cases.
- Feature Engineering: Strong expertise in designing scalable feature engineering pipelines.
- Model Selection & Validation: Deep knowledge of model evaluation frameworks and validation methodologies.
- LightGBM & Gradient Boosting: Hands-on experience with gradient boosting frameworks for structured data.
- MLflow: Proficient in experiment tracking, model registry, and lifecycle management.
- Databricks: Working knowledge of Databricks platform, including model serving and Databricks-native models.
- Team Leadership: Demonstrated ability to lead and develop data science teams in an enterprise context.
Additional Information:
- Strong communication skills, able to articulate complex ML concepts to non-technical stakeholders.
- Strategic thinker with a bias for action and delivery accountability.
- Collaborative, cross-functional mindset with experience working in agile delivery environments.
- Minimum 15 years of experience in data science or ML engineering roles.15 years full time education
Project Role Description : Develops applications and systems that utilize AI tools, Cloud AI services, with proper cloud or on-prem application pipeline with production ready quality. Be able to apply GenAI models as part of the solution. Could also include but not limited to deep learning, neural networks, chatbots, image processing.
Must have skills : Data Science
Good to have skills : NA
Minimum 15 year(s) of experience is required
Educational Qualification : 15 years full time education
Roles & Responsibilities:
- Define and drive ML strategy across identified use cases, ensuring alignment with delivery objectives.
- Lead a team of data scientists and ML engineers, providing technical direction and mentorship.
- Establish feature engineering direction and standards across the data science workstream.
- Design and oversee experiment tracking frameworks and model validation strategies.
- Own end-to-end ML model lifecycle — from evaluation and selection through to production deployment.
- Evaluate and select appropriate ML models, including gradient boosting and ensemble approaches.
- Collaborate with data engineering and architecture teams to ensure model-ready data pipelines.
- Provide hands-on guidance on Databricks, including use of Databricks-native models and MLflow.
Professional & Technical Skills:
- ML Strategy: Proven ability to define ML strategy across multiple concurrent use cases.
- Feature Engineering: Strong expertise in designing scalable feature engineering pipelines.
- Model Selection & Validation: Deep knowledge of model evaluation frameworks and validation methodologies.
- LightGBM & Gradient Boosting: Hands-on experience with gradient boosting frameworks for structured data.
- MLflow: Proficient in experiment tracking, model registry, and lifecycle management.
- Databricks: Working knowledge of Databricks platform, including model serving and Databricks-native models.
- Team Leadership: Demonstrated ability to lead and develop data science teams in an enterprise context.
Additional Information:
- Strong communication skills, able to articulate complex ML concepts to non-technical stakeholders.
- Strategic thinker with a bias for action and delivery accountability.
- Collaborative, cross-functional mindset with experience working in agile delivery environments.
- Minimum 15 years of experience in data science or ML engineering roles.15 years full time education
About Accenture
Accenture is a leading global professional services company that helps the world’s leading businesses, governments and other organizations build their digital core, optimize their operations, accelerate revenue growth and enhance citizen services—creating tangible value at speed and scale. We are a talent- and innovation-led company with approximately 791,000 people serving clients in more than 120 countries. Technology is at the core of change today, and we are one of the world’s leaders in helping drive that change, with strong ecosystem relationships. We combine our strength in technology and leadership in cloud, data and AI with unmatched industry experience, functional expertise and global delivery capability. Our broad range of services, solutions and assets across Strategy & Consulting, Technology, Operations, Industry X and Song, together with our culture of shared success and commitment to creating 360° value, enable us to help our clients reinvent and build trusted, lasting relationships. We measure our success by the 360° value we create for our clients, each other, our shareholders, partners and communities.Visit us at www.accenture.com
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