Infosys
Website:
infosys.com
Company:
https://www.linkedin.com/company/infosys
Seniority: Not Applicable
Industries: IT Services and IT Consulting
Job details:
Desired Qualifications
- Experience in financial services, with understanding of consumer and commercial banking data.
- Experience supporting or enabling AI/ML and GenAI solutions, including feature pipelines and analytics platforms.
- Familiarity with data visualization and BI tools (Tableau, Cognos, SAS).
- Knowledge of responsible AI, data governance, and regulatory considerations in highly regulated environments.
- Experience modernizing legacy data platforms into cloud-native architectures.
- Executive speaking skills — ability to articulate strategy, challenge the status quo, and present to senior leadership and key stakeholders with confidence.
- Experience working across or within highly collaborative, non-hierarchical organizational cultures with an emphasis on peer relationships and open communication. Data Engineering & Architecture
- Serve as a hands-on technical leader in the design of scalable data pipelines, data stores, and information flows across the enterprise.
- Design and optimize cloud-based big data platforms, including ingestion, transformation, storage, and consumption layers.
- Lead the engineering of ETL/ELT frameworks, streaming pipelines, and batch processing solutions.
- Conduct enterprise-wide assessments of data stores and data flows to identify bottlenecks, friction points, and modernization opportunities.
- Own data modeling standards to ensure alignment with business objectives, performance, and accessibility. AI Enablement & Advanced Analytics
- Enable and support AI/ML and GenAI initiatives by building reliable, high-quality, and well-governed data pipelines.
- Collaborate with Data Science teams to operationalize models, including feature engineering pipelines, inference data flows, and model monitoring data.
- Support AI-driven use cases such as predictive analytics, recommendations, NLP-based insights, and intelligent automation.
- Stay current with market trends, embed innovative practices into strategy, and drive the organization forward with an AI-first approach — ensuring AI initiatives move beyond proof-of-concept to enterprise-scale solutions.
- Approach data engineering with an AI mindset and vice versa, reflecting the evolving and inseparable nature of the two disciplines.
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