Role Summary

The Optimization Lead is responsible for designing, executing, and governing efficiency frameworks across production operations. The role requires deep understanding of production management, event ecosystems, analytics, and AI-assisted operations, combined with strong leadership and delivery ownership.


Key Responsibilities

- > Production Operations & Event Intelligence
• Develop complete understanding of event generation, configuration, and flow across widespread technology sources

• Analyze event noise, duplication, and correlation gaps to improve signal-to-noise ratio
• Define and optimize event handling strategies aligned with operational priorities and SLAs
• Ensure operational workflows are scalable, consistent, and outcome-focused

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-> Data Analytics & Platform Enablement
• Design and execute advanced analytics to derive actionable insights from production data
• Build and maintain analytical models using robust development platforms (data pipelines, dashboards, automation frameworks)
• Translate operational data into predictive, prescriptive, and performance insights
• Partner with operations teams to embed analytics into day-to-day execution

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-> AI-Driven Optimization Framework
• Define and govern AI usage frameworks within production operations
• Identify and implement AI use cases such as:
• Event correlation and deduplication
• Anomaly detection and early warning
• Predictive incident trends and risk scoring
• Resolution recommendation and automation enablement
• Ensure AI solutions are explainable, measurable, and operationally adopted
• Drive responsible and value-based AI implementation rather than experimentation-only use

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-> Technology & Engineering Engagement
• Maintain strong technical understanding to challenge, influence, and collaborate with engineering and platform teams
• Participate in architectural and tooling discussions with a focus on operational efficiency and observability
• Translate engineering changes into optimized production workflows and measurable outcomes
• Act as a bridge between engineering intent and operational reality

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-> Leadership & Delivery Ownership
• Demonstrate leadership principles through ownership, accountability, and proactive delivery
• Take on complex challenges without requiring repeated follow-ups or escalations
• Set clear success metrics and drive execution to closure
• Influence teams through data, clarity, and outcomes, not hierarchy
• Foster a culture of continuous improvement, simplification, and bias-free decision-making

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Required Skills & Experience

-> Core Expertise
• Strong experience in production operations and production management
• Deep knowledge of event management, monitoring, and alerting ecosystems
• Hands-on experience with data analytics, automation, and development platforms
• Proven application of AI/ML concepts in operational or production contexts

-> Technical & Analytical
• Ability to interpret complex technical architectures
• Strong analytical thinking with the ability to convert data into decisions
• Experience working with event handling, observability, or workflow tools

Indicative stack includes:

• Monitoring & Observability: Geneos, Dynatrace, AppDynamics, Splunk, ELK stack, Prometheus/Grafana
• Event Management & Incident Platforms: ServiceNow, Netcool/NOI, BigPanda
• Production Monitoring Event Sources : Autosys, SA, ITM6, ITM6K20, ITM6LOG, SCOM, OCP, MQNastel etc.,.
• Data & Analytics Platforms: Python, Tableau, Power BI, Snowflake, SQL.
• Automation & Workflow Enablement: Ansible, Control-M, ServiceNow automation frameworks
• Understanding upon AI/ML Enablement: Python ML libraries, Databricks, Azure ML, anomaly detection, and predictive analytics frameworks

-> Leadership & Mindset
• Self-driven with a delivery-first mentality
• Comfortable challenging status quo and driving change
• Strong communication skills across technical and non-technical stakeholders
• Demonstrated ability to lead without formal authority

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What Makes This Role Distinct
• Not a reporting or monitoring role
• Not a reactive operations role
• Focused on optimization, intelligence, and transformation
• Combines operations + analytics + AI + leadership

Role Significance & Business Impact (Note)

This role is critical due to the scale and complexity of production data handled, requiring the Optimization Lead to examine and derive insights from approximately 4,000,000 (40 lakhs) operational data points annually across multiple technology sources.

At this scale:
• Manual or reactive analysis is not feasible
• Advanced data analytics and AI-driven frameworks become mandatory, not optional
• Accuracy in event correlation, prioritization, and response directly impacts system stability, customer experience, and operational cost

The Optimization Lead ensures that high-volume production data is transformed into actionable intelligence, enabling proactive issue identification, noise reduction, efficiency gains, and continuous improvement across production operations.

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Job Family Group:

Technology

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Job Family:

Infrastructure

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Time Type:

Full time

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Most Relevant Skills

Please see the requirements listed above.

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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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