Website:
eisai.co.in
Job details:
Job Description
Global AI Operations Specialist
Data & Analytics - Eisai GCC
Job Summary
The Global AI Operations Specialist is a hands-on role responsible for deploying, monitoring, governing, and continuously improving Eisai AI solutions in Production. The role will help ensure that GenAI and Agentic AI solutions are released with strong operational discipline, monitored for reliability and performance, and enhanced quickly when minor changes are required. This position will partner with the Global D&A team, Regional AI Developers, Platform Owners, IT Infrastructure, Security, and vendors to improve deployment quality, reduce Production issues, and increase business stakeholder trust in AI solutions.
Business Context and Purpose
• Ensure Eisai AI solutions are effectively deployed, governed, monitored, supported, and continuously improved.
• Separate operational ownership from core AI development so developers can focus on new solution build while Production operations remain stable and accountable.
• Improve trust from business stakeholders by reducing deployment issues, enforcing standards, and maintaining reliable AI operations.
• Support the GDASC capability build by establishing strong India-owned AI operations and Use Databricks, Copilot Studio, and Python minor enhancement capability in partnership with global teams.
Essential Functions
AI Operations and Production Deployment
• Own and coordinate Production deployment activities for AI solutions, including release readiness, deployment planning, validation, monitoring, and post-deployment support.
• Monitor active AI solutions for operational reliability, quality, and performance; identify and resolve Production issues with urgency and clear ownership.
• Establish, document, and enforce AI Production deployment standards, checklists, runbooks, and support processes.
• Ensure AI solutions meet expected operational quality standards before and after release, including adherence to agreed deployment, governance, and support practices.
• Track and report Production deployment issues, recurring risks, operational metrics, and improvement actions.
Minor Enhancement Development
• Develop, test, and implement minor enhancements to existing AI solutions, with emphasis on GenAI and Agentic AI capabilities.
• Use Python and Databricks to support AI solution enhancements, troubleshooting, automation, and operational improvement activities.
• Partner with Regional AI Developers and Platform Owners to understand solution design, operational dependencies, enhancement priorities, and release impacts.
• Support small change delivery with clear documentation, testing evidence, release notes, and stakeholder communication.
• Contribute to reusable patterns, templates, and practices that improve AI operations and deployment consistency.
Stakeholder Collaboration and Delivery Coordination
• Collaborate with Global D&A, Regional AI Developers, vendors, IT Infrastructure, Security, and platform teams to coordinate operations, issue resolution, and deployments.
• Translate operational needs and stakeholder feedback into clear technical actions, enhancement requests, and deployment requirements.
• Communicate status, risks, blockers, and outcomes to stakeholders in a transparent and timely manner.
• Build trusted relationships with global and regional stakeholders by maintaining a strong service mindset and reliable execution cadence.
• Support technical deep-dives, case discussions, and operational reviews to identify root causes and prevent repeat issues.
Standards, Governance, and Continuous Improvement
• Ensure AI deployment and operational practices align with Eisai standards, governance expectations, security requirements, and platform guidelines.
• Review operational readiness and solution documentation for completeness, maintainability, and supportability.
• Identify opportunities to improve deployment quality, monitoring, support processes, and enhancement delivery velocity.
• Promote consistent use of standards across Global AI operations while remaining adaptable to regional and platform needs.
• Help build operational maturity for AI solutions as the team scales to two to three members over time.
Requirements
• Minimum 8+ years of related technology, data, AI, analytics, or platform operations experience.
• Minimum 3+ years of hands-on AI development or AI solution delivery experience, preferably including GenAI or Agentic AI solutions.
• Hands-on experience with Python required.
• Experience with Databricks or similar modern data and AI platforms strongly preferred.
• Experience deploying, supporting, monitoring, or operating AI, data, or software solutions in a Production environment.
• Ability to troubleshoot technical issues, identify root causes, and coordinate timely resolution across technical and business stakeholders.
• Experience creating or following release standards, deployment checklists, operational runbooks, and support processes.
• Strong communication skills with the ability to explain technical issues, risks, and actions clearly to global stakeholders.
• Ability to work effectively in a changing environment with time-sensitive priorities and cross-functional dependencies.
• Demonstrated ownership, attention to detail, and bias for operational quality and continuous improvement.
• College diploma or university degree in computer science, information systems, data science, engineering, business administration, or a related field preferred.
Preferred Qualifications
• Hands-on experience with GenAI or Agentic AI development is strongly preferred and considered a core fit requirement.
• Databricks AI development experience and/or Databricks certification.
• Experience with Microsoft Copilot Studio or similar AI agent / workflow development platforms.
• Experience in pharmaceutical, life sciences, SaaS, or large-scale data platform environments.
• Experience working with global teams, vendors, managed service partners, and platform owners.
• Familiarity with Agile ways of working, technical documentation, testing practices, and release management.
Working Model and Stakeholders
• Hands-on vs. oversight split: approximately 80% hands-on execution and 20% oversight / coordination.
• Global vs. India ownership: strategy and new development remain global; minor enhancements and operations are India-owned in close partnership with global teams.
• Core stakeholders include Global D&A, Regional AI Developers, Platform Owners, IT Infrastructure, Security, and vendors.
• The team is expected to scale to two to three members over time.
Competencies
• Technical depth in AI solution development, deployment, monitoring, and troubleshooting.
• Operational ownership, urgency, and accountability for Production quality.
• Clear communication and stakeholder management across global and regional teams.
• Strong problem-solving ability and practical judgment under time and resource constraints.
• Collaboration, Excellence, Innovation, Integrity, Passion, and Patient / Stakeholder Centricity.
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