Principle Engineer

Chennai - India

Monthly Salary: Not Disclosed

Posted on: 26-08-2025

Vacancies: 1 Vacancy

Job Summary

Description

You will manage a talented team of data scientists and AI engineers driving the adoption of intelligent automation predictive analytics and proactive problem resolution across our complex IT landscape. This position requires a leader with a deep understanding of both data science principles and the intricacies of enterprise IT operations.

Responsibilities

Strategic Leadership: Define and execute the AIOps strategy and roadmap aligning it with overall IT and business objectives. Identify opportunities to leverage AI/ML for enhanced IT observability incident management performance optimization and automation.
Team Management & Development: Lead mentor and grow a high-performing team of data scientists and AI engineers. Foster a culture of innovation continuous learning and technical excellence.
Solution Design & Development: Oversee the end-to-end design development and deployment of AIOps solutions including anomaly detection predictive failure analysis root cause analysis intelligent alerting and automated remediation.
Cross-Functional Collaboration: Partner closely with IT Operations Site Reliability Engineering (SRE) Network Engineering Application Development and other stakeholders to understand operational challenges and deliver impactful AI-driven solutions.
Data & Platform Management: Ensure the availability quality and governance of operational data necessary for AI/ML model training and inference. Drive the selection integration and optimization of AIOps platforms and tools.
Model Lifecycle Management: Establish robust MLOps practices for model development testing deployment monitoring and retraining to ensure the continuous effectiveness and reliability of AI models in production.
Innovation & Research: Stay abreast of the latest advancements in AI/ML AIOps and IT operations. Drive research and experimentation to explore new techniques and technologies that can further enhance our operational intelligence.
Performance & Metrics: Define key performance indicators (KPIs) for AIOps initiatives and regularly report on the impact and value delivered to the organization.
Budget & Resource Management: Manage project budgets resources and timelines effectively to ensure successful delivery of AIOps programs.

Qualifications

Required Qualifications:

Bachelors or Masters degree in Computer Science Data Science Artificial Intelligence Engineering or a related quantitative field.
10 years of progressive experience in data science machine learning and/or AI engineering.
5 years of experience in a leadership or management role leading technical teams focused on data science or AI.
Proven experience in designing developing and deploying AI/ML models for real-world applications particularly within IT operations or related domains (e.g. observability security infrastructure management).
Strong understanding of IT operations concepts including monitoring alerting incident management change management and IT service management (ITSM).
Proficiency in programming languages commonly used in data science and AI (e.g. Python Scala Java).
Hands-on experience with big data technologies (e.g. Spark Hadoop Kafka) and cloud platforms (AWS Azure GCP).
Solid grasp of machine learning algorithms (e.g. supervised unsupervised deep learning) and statistical modeling.
Excellent communication interpersonal and leadership skills with the ability to articulate complex technical concepts to non-technical stakeholders.
Demonstrated ability to drive strategic initiatives manage complex projects and deliver results in a fast-paced environment.

Preferred Qualifications:

Experience with specific AIOps platforms or tools (e.g. Splunk Dynatrace Moogsoft PagerDuty ServiceNow Datadog ELK stack).
Familiarity with IT service management frameworks (e.g. ITIL).
Experience with containerization (Docker Kubernetes) and microservices architectures.
Knowledge of MLOps best practices and tools for automating and managing the ML lifecycle.
Experience in a large-scale enterprise environment with diverse and complex IT infrastructure.

DescriptionYou will manage a talented team of data scientists and AI engineers driving the adoption of intelligent automation predictive analytics and proactive problem resolution across our complex IT landscape. This position requires a leader with a deep understanding of both data science principl...

Description

Responsibilities

Strategic Leadership: Define and execute the AIOps strategy and roadmap aligning it with overall IT and business objectives. Identify opportunities to leverage AI/ML for enhanced IT observability incident management performance optimization and automation.
Team Management & Development: Lead mentor and grow a high-performing team of data scientists and AI engineers. Foster a culture of innovation continuous learning and technical excellence.
Solution Design & Development: Oversee the end-to-end design development and deployment of AIOps solutions including anomaly detection predictive failure analysis root cause analysis intelligent alerting and automated remediation.
Cross-Functional Collaboration: Partner closely with IT Operations Site Reliability Engineering (SRE) Network Engineering Application Development and other stakeholders to understand operational challenges and deliver impactful AI-driven solutions.
Data & Platform Management: Ensure the availability quality and governance of operational data necessary for AI/ML model training and inference. Drive the selection integration and optimization of AIOps platforms and tools.
Model Lifecycle Management: Establish robust MLOps practices for model development testing deployment monitoring and retraining to ensure the continuous effectiveness and reliability of AI models in production.
Innovation & Research: Stay abreast of the latest advancements in AI/ML AIOps and IT operations. Drive research and experimentation to explore new techniques and technologies that can further enhance our operational intelligence.
Performance & Metrics: Define key performance indicators (KPIs) for AIOps initiatives and regularly report on the impact and value delivered to the organization.
Budget & Resource Management: Manage project budgets resources and timelines effectively to ensure successful delivery of AIOps programs.

Qualifications

Required Qualifications:

Bachelors or Masters degree in Computer Science Data Science Artificial Intelligence Engineering or a related quantitative field.
10 years of progressive experience in data science machine learning and/or AI engineering.
5 years of experience in a leadership or management role leading technical teams focused on data science or AI.
Proven experience in designing developing and deploying AI/ML models for real-world applications particularly within IT operations or related domains (e.g. observability security infrastructure management).
Strong understanding of IT operations concepts including monitoring alerting incident management change management and IT service management (ITSM).
Proficiency in programming languages commonly used in data science and AI (e.g. Python Scala Java).
Hands-on experience with big data technologies (e.g. Spark Hadoop Kafka) and cloud platforms (AWS Azure GCP).
Solid grasp of machine learning algorithms (e.g. supervised unsupervised deep learning) and statistical modeling.
Excellent communication interpersonal and leadership skills with the ability to articulate complex technical concepts to non-technical stakeholders.
Demonstrated ability to drive strategic initiatives manage complex projects and deliver results in a fast-paced environment.

Preferred Qualifications:

Experience with specific AIOps platforms or tools (e.g. Splunk Dynatrace Moogsoft PagerDuty ServiceNow Datadog ELK stack).
Familiarity with IT service management frameworks (e.g. ITIL).
Experience with containerization (Docker Kubernetes) and microservices architectures.
Knowledge of MLOps best practices and tools for automating and managing the ML lifecycle.
Experience in a large-scale enterprise environment with diverse and complex IT infrastructure.

Key Skills

Enterprise Software
SAP Warehouse Management
AWS
Solution Architecture
SAP EWM
SAP Finance & Controlling
SAP Supply Chain Management
SAP S/4HANA
Enterprise Sales
Pre-sales
Management Consulting
Oracle

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