Principal Engineer- Data And AI

Infosys; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Infosys in Bengaluru seeks a hands-on technology leader to design and optimize cloud-based data platforms, pipelines, and data stores across the enterprise. You will own end-to-end data flows, ensure governance, and drive scalable ETL/ELT and streaming solutions in a large-scale ecosystem.

You will collaborate with AI/ML teams to operationalize models, guide data engineering strategy, and mentor multiple scrum pods across enablement teams, balancing tech depth with enterprise impact.

Qualifications

• 12+ years in data engineering/platform engineering with 5+ years in senior tech leadership.
• Proven experience leading teams to build large-scale data platforms in cloud environments.
• Experience delivering scalable pipelines and governance across enterprise data stores.

Responsibilities

• Design scalable data pipelines, data stores, and information flows across the enterprise.
• Lead ETL/ELT frameworks, streaming and batch processing solutions.
• Collaborate with AI/ML teams to operationalize models and AI-enabled data flows.
• Ensure data governance, security, privacy, and compliance across platforms.
• Mentor multiple scrum pods; drive recruitment and upskilling within enablement teams.

Skills

ETL/ELT tools

Programming & data tech

AWS data services

Distributed systems

Budget management

Education

Bachelor's degree in CS/Engineering/Statistics

Master's degree (preferred)

AWS/Big Data/Agile certs (preferred)

Tools

Informatica

DataStage

SQL

Python

Spark

Scala

Java

Shell scripting

AWS data services

Job description

• - 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.

Delivery, Reliability & Governance :

• - Ensure teams deliver high-quality solutions with clear requirements, strong engineering discipline, and predictable delivery.
• - Implement best practices across CI/CD, DevOps, data quality checks, monitoring, and observability.
• - Embed data governance, security, privacy, and compliance controls across all data platforms.
• - Ensure platforms meet enterprise standards for availability, scalability, and resiliency.
• - Lead an enabling team responsible for building foundational platforms, tools, and guardrails, supporting multiple arms of AI engineering and enabling the broader organization.
• - Lead multiple scrum pods or functional teams, with accountability for recruitment, upskilling, and technical leadership across the enablement structure.

Learning Agility :

• - Stays current with rapidly evolving AI, data engineering, and cloud technologies; continuously embeds new knowledge into platform strategy and team practices.
• - Understands and bridges both data and AI engineering disciplines, adapting quickly as these fields converge.

Customer Centricity :

• - Ensures data platforms and pipelines are designed around the needs of internal teams, end users, and the business delivering reliable, governed, and accessible data products.
• - Communicates strategy and technical direction with empathy and clarity across all levels, from engineers to executives.

Tenacity / Persistence :

• - Balances empathy with a strong delivery focus drives teams to meet high standards with predictable outcomes even in complex, large-scale environments.
• - Removes impediments, resolves conflicts constructively, and maintains momentum across multiple teams and workstreams without losing sight of the long-term platform vision.

Required Qualifications :

• - 12+ years of experience in data engineering, database engineering, or platform engineering, including 5+ years in senior technical leadership roles.
• - Proven experience leading teams building large-scale data platforms in cloud environments.

Deep hands-on expertise with :

• - 2. ETL/ELT tools and frameworks (Informatica, DataStage, custom frameworks, etc.)
• - 4. Programming & data technologies : SQL, Python, Spark, Scala, Java, shell scripting.
• - Strong experience with AWS data services (S3, Glue, Athena, RDS, Redshift, etc.).
• - Solid understanding of distributed systems, data architecture, and performance optimization.
• - Demonstrated ability to partner with senior stakeholders and influence across technology and business teams.
• - Hands-on experience with AI technologies; ability to understand and implement new advancements and articulate technical details to both engineering teams and executives.
• - Financial discipline ability to manage budget and financial responsibilities at a team and platform level.

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.

Education & Certifications :

• - Required : Bachelor's degree in Computer Science, Engineering, Statistics, or related field.
• - Preferred : Master's degree in Computer Science, Data Engineering, AI/ML, or related discipline.
• - Preferred : AWS, Big Data, or Agile certifications.