AI / ML Engineer

AI / ML Engineer

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 : Microsoft Azure Databricks
Good to have skills : NA
Minimum 5 year(s) of experience is required
Educational Qualification : 15 years full time education

Summary:
Across roles, strong communication, strategic thinking, collaboration, and experience requirements are emphasized, with Accenture affirming equal opportunity employment regardless of race, gender, religion, or disability

Roles & Responsibilities:
- Lead the data engineering workstream, providing technical direction and hands-on oversight.
- Architect and implement a medallion data architecture (Bronze, Silver, Gold layers) at scale.
- Design and build Unified Demand, Supply, and Pipeline gold-layer entities for consumption.
- Expertise in Data governance and control mechanisms with databricks unity catalog.
- Provide technical guidance, code reviews, and mentorship to a team of data engineers.
- Establish and enforce data engineering standards, including naming conventions and testing practices.
- Collaborate with data architects, ML engineers, and product teams to align data outputs to requirements.
- Manage pipeline orchestration and scheduling using Lakeflow and equivalent tooling.
- Ensure data quality, lineage, and observability across the full data engineering stack.

Professional & Technical Skills:
- Databricks: Deep expertise in the Databricks platform across data engineering and orchestration.
- Apache Spark: Strong command of Spark for large-scale distributed data processing.
- Delta Lake: Proficiency in Delta Lake for ACID-compliant, versioned data lake management.
- Lakeflow: Hands-on experience with Lakeflow for pipeline orchestration and scheduling.
- Python & SQL: Expert-level proficiency in Python and SQL for data engineering tasks.
- Medallion Architecture: Proven experience designing and implementing multi-layer lakehouse architectures.
- Team Leadership: Track record of leading and growing high-performing data engineering teams.
Additional Information:
- Strong technical and people leadership skills, with an ability to balance delivery and team development.
- Clear communicator, comfortable engaging with senior stakeholders and cross-functional teams.
- Pragmatic problem-solver with a focus on scalability, maintainability, and engineering excellence.
- Minimum 6-8 years of data engineering experience, with at least 3 years in a team lead capacity.